Search result: Catalogue data in Spring Semester 2021

Number	Title	Type	ECTS	Hours	Lecturers
Computational Science and Engineering Bachelor
Bachelor Studies (Programme Regulations 2018)
First Year Compulsory Courses
First Year Examination Block 1 Offered in the Autumn Semester
First Year Examination Block 2
401-0232-10L	Analysis 2	O	8 credits	4V + 2U	T. Rivière
Abstract	Introduction to differential calculus and integration in several variables.
Objective	Einführung in die Grundlagen der Analysis
Content	Differentiation in several variables, maxima and minima, the implicit function theorem, integration in several variables, integration over submanifolds, the theorems of Gauss and Stokes.
Lecture notes	Christian Blatter: Ingenieur-Analysis (Kapitel 4-6). Konrad Koenigsberger, Analysis II.
401-0302-10L	Complex Analysis	O	4 credits	3V + 1U	A. Iozzi
Abstract	Basics of complex analysis in theory and applications, in particular the global properties of analytic functions. Introduction to the integral transforms and description of some applications
Objective	Erwerb von einigen grundlegenden Werkzeuge der komplexen Analysis.
Content	Examples of analytic functions, Cauchy‘s theorem, Taylor and Laurent series, singularities of analytic functions, residues. Fourier series and Fourier integral, Laplace transform.
Literature	J. Brown, R. Churchill: "Complex Analysis and Applications", McGraw-Hill 1995 T. Needham. Visual complex analysis. Clarendon Press, Oxford. 2004. M. Ablowitz, A. Fokas: "Complex variables: introduction and applications", Cambridge Text in Applied Mathematics, Cambridge University Press 1997 E. Kreyszig: "Advanced Engineering Analysis", Wiley 1999 J. Marsden, M. Hoffman: "Basic complex analysis", W. H. Freeman 1999 P. P. G. Dyke: "An Introduction to Laplace Transforms and Fourier Series", Springer 2004 A. Oppenheim, A. Willsky: "Signals & Systems", Prentice Hall 1997 M. Spiegel: "Laplace Transforms", Schaum's Outlines, Mc Graw Hill
Prerequisites / Notice	Prerequisites: Analysis I and II
402-0044-00L	Physics II	O	4 credits	3V + 1U	T. Esslinger
Abstract	Introduction to the concepts and tools in physics with the help of demonstration experiments: electromagnetism, optics, introduction to modern physics.
Objective	The concepts and tools in physics, as well as the methods of an experimental science are taught. The student should learn to identify, communicate and solve physical problems in his/her own field of science.
Content	Electromagnetism (electric current, magnetic fields, electromagnetic induction, magnetic materials, Maxwell's equations) Optics (light, geometrical optics, interference and diffraction) Short introduction to quantum physics
Lecture notes	The lecture follows the book "Physik" by Paul A. Tipler.
Literature	Paul A. Tipler and Gene Mosca Physik Springer Spektrum Verlag
529-4000-00L	Chemistry	O	4 credits	3G	E. C. Meister
Abstract	Introduction to chemistry with aspects of inorganic, organic and physical chemistry.
Objective	- Understanding of simple models of chemical bonding and the three-dimensional molecular structure - Quantitative description of selected chemical systems by means of reaction equations and equilibria - Understanding of fundamental concepts of chemical kinetics (e.g. reaction order, rate law, rate constant)
Content	Periodic system of the elements, chemical bonding (LCAO-MO), molecular structure (VSEPR), reactions, equilibria, chemical kinetics.
Lecture notes	Handouts of lecture presentations and additional supporting information will be offered.
Literature	C.E. Housecroft, E.C. Constable, Chemistry. An Introduction to Organic, Inorganic and Physical Chemistry, 4th ed., Pearson: Harlow 2010. C.E. Mortimer, U. Müller, Chemie, 11. Auflage, Thieme: Stuttgart 2014.
252-0002-00L	Data Structures and Algorithms	O	8 credits	4V + 2U	F. Friedrich Wicker
Abstract	The course provides the foundations for the design and analysis of algorithms. Classical problems ranging from sorting up to problems on graphs are used to discuss common data structures, algorithms and algorithm design paradigms. The course also comprises an introduction to parallel and concurrent programming and the programming model of C++ is discussed in some depth.
Objective	An understanding of the analysis and design of fundamental and common algorithms and data structures. Deeper insight into a modern programming model by means of the programming language C++. Knowledge regarding chances, problems and limits of parallel and concurrent programming.
Content	Data structures and algorithms: mathematical tools for the analysis of algorithms (asymptotic function growth, recurrence equations, recurrence trees), informal proofs of algorithm correctness (invariants and code transformation), design paradigms for the development of algorithms (induction, divide-and-conquer, backtracking and dynamic programming), classical algorithmic problems (searching, selection and sorting), data structures for different purposes (linked lists, hash tables, balanced search trees, quad trees, heaps, union-find), further tools for runtime analysis (generating functions, amortized analysis. The relationship and tight coupling between algorithms and data structures is illustrated with graph algorithms (traversals, topological sort, closure, shortest paths, minimum spanning trees, max flow). Programming model of C++: correct and efficient memory handling, generic programming with templates, exception handling, functional approaches with functors and lambda expressions. Parallel programming: structure of parallel architectures (multicore, vectorization, pipelining) concepts of parallel programming (Amdahl's and Gustavson's laws, task/data parallelism, scheduling), problems of concurrency (data races, bad interleavings, memory reordering), process synchronisation and communication in a shared memory system (mutual exclusion, semaphores, monitors, condition variables), progress conditions (freedom from deadlock, starvation, lock- and wait-freedom). The concepts are underpinned with examples of concurrent and parallel programs and with parallel algorithms, implemented in C++. In general, the concepts provided in the course are motivated and illustrated with practically relevant algorithms and applications. Exercises are carried out in Code-Expert, an online IDE and exercise management system. All required mathematical tools above high school level are covered, including a basic introduction to graph theory.
Literature	Cormen, Leiserson, Rivest, and Stein: Introduction to Algorithms, 3rd ed., MIT Press, 2009. ISBN 978-0-262-03384-8 (recommended text) Maurice Herlihy, Nir Shavit, The Art of Multiprocessor Programming, Elsevier, 2012. B. Stroustrup, The C++ Programming Language (4th Edition) Addison-Wesley, 2013.
Prerequisites / Notice	Prerequisites: Lecture Series 252-0835-00L Informatik I or equivalent knowledge in programming with C++.

Basic Courses
Block G1 All course units within Block G1 are offered in the autumn semester.
Block G2 All course units within Block G2 are offered in the autumn semester.
Block G3
Number	Title	Type	ECTS	Hours	Lecturers
401-0674-00L	Numerical Methods for Partial Differential Equations Not meant for BSc/MSc students of mathematics.	O	10 credits	2G + 2U + 2P + 4A	R. Hiptmair
Abstract	Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convection-diffusion, heat equation, wave equation, conservation laws. Implementation in C++ based on a finite element library.
Objective	Main skills to be acquired in this course: * Ability to implement fundamental numerical methods for the solution of partial differential equations efficiently. * Ability to modify and adapt numerical algorithms guided by awareness of their mathematical foundations. * Ability to select and assess numerical methods in light of the predictions of theory * Ability to identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm. * Ability to understand research publications on theoretical and practical aspects of numerical methods for partial differential equations. * Skills in the efficient implementation of finite element methods on unstructured meshes. This course is neither a course on the mathematical foundations and numerical analysis of methods nor an course that merely teaches recipes and how to apply software packages.
Content	1 Second-Order Scalar Elliptic Boundary Value Problems 1.2 Equilibrium Models: Examples 1.3 Sobolev spaces 1.4 Linear Variational Problems 1.5 Equilibrium Models: Boundary Value Problems 1.6 Diffusion Models (Stationary Heat Conduction) 1.7 Boundary Conditions 1.8 Second-Order Elliptic Variational Problems 1.9 Essential and Natural Boundary Conditions 2 Finite Element Methods (FEM) 2.2 Principles of Galerkin Discretization 2.3 Case Study: Linear FEM for Two-Point Boundary Value Problems 2.4 Case Study: Triangular Linear FEM in Two Dimensions 2.5 Building Blocks of General Finite Element Methods 2.6 Lagrangian Finite Element Methods 2.7 Implementation of Finite Element Methods 2.7.1 Mesh Generation and Mesh File Format 2.7.2 Mesh Information and Mesh Data Structures 2.7.2.1 L EHR FEM++ Mesh: Container Layer 2.7.2.2 L EHR FEM++ Mesh: Topology Layer 2.7.2.3 L EHR FEM++ Mesh: Geometry Layer 2.7.3 Vectors and Matrices 2.7.4 Assembly Algorithms 2.7.4.1 Assembly: Localization 2.7.4.2 Assembly: Index Mappings 2.7.4.3 Distribute Assembly Schemes 2.7.4.4 Assembly: Linear Algebra Perspective 2.7.5 Local Computations 2.7.5.1 Analytic Formulas for Entries of Element Matrices 2.7.5.2 Local Quadrature 2.7.6 Treatment of Essential Boundary Conditions 2.8 Parametric Finite Element Methods 3 FEM: Convergence and Accuracy 3.1 Abstract Galerkin Error Estimates 3.2 Empirical (Asymptotic) Convergence of Lagrangian FEM 3.3 A Priori (Asymptotic) Finite Element Error Estimates 3.4 Elliptic Regularity Theory 3.5 Variational Crimes 3.6 FEM: Duality Techniques for Error Estimation 3.7 Discrete Maximum Principle 3.8 Validation and Debugging of Finite Element Codes 4 Beyond FEM: Alternative Discretizations [dropped] 5 Non-Linear Elliptic Boundary Value Problems [dropped] 6 Second-Order Linear Evolution Problems 6.1 Time-Dependent Boundary Value Problems 6.2 Parabolic Initial-Boundary Value Problems 6.3 Linear Wave Equations 7 Convection-Diffusion Problems [dropped] 8 Numerical Methods for Conservation Laws 8.1 Conservation Laws: Examples 8.2 Scalar Conservation Laws in 1D 8.3 Conservative Finite Volume (FV) Discretization 8.4 Timestepping for Finite-Volume Methods 8.5 Higher-Order Conservative Finite-Volume Schemes
Lecture notes	The lecture will be taught in flipped classroom format: - Video tutorials for all thematic units will be published online. - Tablet notes accompanying the videos will be made available to the audience as PDF. - A comprehensive lecture document will cover all aspects of the course.
Literature	Chapters of the following books provide supplementary reading (detailed references in course material): * D. Braess: Finite Elemente, Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Springer 2007 (available online). * S. Brenner and R. Scott. Mathematical theory of finite element methods, Springer 2008 (available online). * A. Ern and J.-L. Guermond. Theory and Practice of Finite Elements, volume 159 of Applied Mathematical Sciences. Springer, New York, 2004. * Ch. Großmann and H.-G. Roos: Numerical Treatment of Partial Differential Equations, Springer 2007. * W. Hackbusch. Elliptic Differential Equations. Theory and Numerical Treatment, volume 18 of Springer Series in Computational Mathematics. Springer, Berlin, 1992. * P. Knabner and L. Angermann. Numerical Methods for Elliptic and Parabolic Partial Differential Equations, volume 44 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * S. Larsson and V. Thomée. Partial Differential Equations with Numerical Methods, volume 45 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * R. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge, UK, 2002. However, study of supplementary literature is not important for for following the course.
Prerequisites / Notice	Mastery of basic calculus and linear algebra is taken for granted. Familiarity with fundamental numerical methods (solution methods for linear systems of equations, interpolation, approximation, numerical quadrature, numerical integration of ODEs) is essential. Important: Coding skills and experience in C++ are essential. Homework assignments involve substantial coding, partly based on a C++ finite element library. The written examination will be computer based and will comprise coding tasks.
401-0614-00L	Probability and Statistics	O	5 credits	2V + 2U	M. Schweizer
Abstract	Einführung in die Wahrscheinlichkeitstheorie und Statistik
Objective	a) Fähigkeit, die behandelten wahrscheinlichkeitstheoretischen Methoden zu verstehen und anzuwenden b) Probabilistisches Denken und stochastische Modellierung c) Fähigkeit, einfache statistische Tests selbst durchzuführen und die Resultate zu interpretieren
Content	Wahrscheinlichkeitsraum, Wahrscheinlichkeitsmass, Zufallsvariablen, Verteilungen, Dichten, Unabhängigkeit, bedingte Wahrscheinlichkeiten, Erwartungswert, Varianz, Kovarianz, Gesetz der grossen Zahlen, Zentraler Grenzwertsatz, grosse Abweichungen, Chernoff-Schranken, Maximum-Likelihood-Schätzer, Momentenschätzer, Tests, Neyman-Pearson Lemma, Konfidenzintervalle
Lecture notes	Lernmaterialien sind erhältlich auf https://metaphor.ethz.ch/x/2021/fs/401-0614-00L/

Block G4
Number	Title	Type	ECTS	Hours	Lecturers
529-0431-00L	Physical Chemistry III: Molecular Quantum Mechanics	O	4 credits	4G	F. Merkt
Abstract	Postulates of quantum mechanics, operator algebra, Schrödinger's equation, state functions and expectation values, matrix representation of operators, particle in a box, tunneling, harmonic oscillator, molecular vibrations, angular momentum and spin, generalised Pauli principle, perturbation theory, electronic structure of atoms and molecules, Born-Oppenheimer approximation.
Objective	This is an introductory course in quantum mechanics. The course starts with an overview of the fundamental concepts of quantum mechanics and introduces the mathematical formalism. The postulates and theorems of quantum mechanics are discussed in the context of experimental and numerical determination of physical quantities. The course develops the tools necessary for the understanding and calculation of elementary quantum phenomena in atoms and molecules.
Content	Postulates and theorems of quantum mechanics: operator algebra, Schrödinger's equation, state functions and expectation values. Linear motions: free particles, particle in a box, quantum mechanical tunneling, the harmonic oscillator and molecular vibrations. Angular momentum: electronic spin and orbital motion, molecular rotations. Electronic structure of atoms and molecules: the Pauli principle, angular momentum coupling, the Born-Oppenheimer approximation. Variational principle and perturbation theory. Discussion of bigger systems (solids, nano-structures).
Lecture notes	A script written in German will be available. The script is, however, no replacement for personal notes during the lecture and does not cover all aspects discussed.
151-0102-00L	Fluid Dynamics I	O	6 credits	4V + 2U	T. Rösgen
Abstract	An introduction to the physical and mathematical foundations of fluid dynamics is given. Topics include dimensional analysis, integral and differential conservation laws, inviscid and viscous flows, Navier-Stokes equations, boundary layers, turbulent pipe flow. Elementary solutions and examples are presented.
Objective	An introduction to the physical and mathematical principles of fluid dynamics. Fundamental terminology/principles and their application to simple problems.
Content	Phenomena, applications, foundations dimensional analysis and similitude; kinematic description; conservation laws (mass, momentum, energy), integral and differential formulation; inviscid flows: Euler equations, stream filament theory, Bernoulli equation; viscous flows: Navier-Stokes equations; boundary layers; turbulence
Lecture notes	Lecture notes (extended formulary) for the course are made available electronically.
Literature	Recommended book: Fluid Mechanics, Kundu & Cohen & Dowling, 6th ed., Academic Press / Elsevier (2015).
Prerequisites / Notice	Voraussetzungen: Physik, Analysis
529-0483-00L	Statistical Physics and Computer Simulation	O	4 credits	2V + 1U	S. Riniker, P. H. Hünenberger
Abstract	Principles and applications of statistical mechanics and equilibrium molecular dynamics, Monte Carlo simulation, Stochastic dynamics. Exercises using a MD simulation program to generate ensembles and subsequently calculate ensemble averages.
Objective	Introduction to statistical mechanics with the aid of computer simulation, development of skills to carry out statistical mechanical calculations using computers and interpret the results.
Content	Principles and applications of statistical mechanics and equilibrium molecular dynamics, Monte Carlo simulation, stochastic dynamics, free energy calculation. Exercises using a MD simulation program to generate ensembles and subsequently calculate ensemble averages.
Literature	will be announced in the lecture
Prerequisites / Notice	Since the exercises on the computer do convey and test essentially different skills as those being conveyed during the lectures and tested at the written exam, the results of a small programming project will be presented in a 10-minutes talk by pairs of students who had been working on the project. Additional information will be provided in the first lecture.

Core Courses from Group I (Modules)
Module A
Number	Title	Type	ECTS	Hours	Lecturers
151-0116-00L	High Performance Computing for Science and Engineering (HPCSE) for CSE	W	7 credits	4G + 2P	P. Koumoutsakos, S. M. Martin
Abstract	This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Bayesian Uncertainty Quantification and Machine Learning including the implementation of these algorithms on HPC architectures.
Objective	The course will teach - programming models and tools for multi and many-core architectures - fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. - fundamentals of Deep Learning
Content	High Performance Computing: - Advanced topics in shared-memory programming - Advanced topics in MPI - GPU architectures and CUDA programming Uncertainty Quantification: - Uncertainty quantification under parametric and non-parametric modeling uncertainty - Bayesian inference with model class assessment - Markov Chain Monte Carlo simulation Machine Learning - Deep Neural Networks and Stochastic Gradient Descent - Deep Neural Networks for Data Compression (Autoencoders) - Recurrent Neural Networks
Lecture notes	https://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs21/ Class notes, handouts
Literature	- Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot - Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling - An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas - Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides
Prerequisites / Notice	Attendance of HPCSE I

Module B
Number	Title	Type	ECTS	Hours	Lecturers
401-3670-00L	High-Performance Computing Lab for CSE	W	7 credits	4G + 1P	R. Käppeli, O. Schenk
Abstract	This HPC Lab for CSE will focus on the effective exploitation of state-of-the-art HPC systems with a special focus on Computational Science and Engineering. The content of the course is tailored for 3th year Bachelor students interested in both learning parallel programming models, scientific mathematical libraries, and having hands-on experience using HPC systems.
Objective	A goal of the course is that students will learn principles and practices of basic numerical methods and HPC to enable large-scale scientific simulations. This goal will be achieved within six to eight mini-projects with a focus on HPC and CSE.
Content	Despite the success of parallel programming languages standardization, there is growing evidence that future computational science applications will depend on a computational software stack. The computational software approach in this HPC Lab is based on building and using small, simple software parts with flexible, easy-to-use interfaces. These simple software parts are toolkits - libraries containing basic services commonly needed by applications - and they build the underlying software layer for computational science and engineering applications. This course will introduce some of the many ways in which mathematical HPC software and numerical algorithms in computer science and mathematics play a role in computational science. The students will learn within several mini-projects how these algorithms and software can be used to enable large-scale scientific applications. It covers topics such as single core optimization for the memory hierarchy, parallel large-scale graph partititoning, parallel mathematical linear solvers, large-scale nonlinear optimization, and parallel software for the mathematical solution of nonlinear partial differential equations. The course takes both an algorithmic and a computing approach, focusing on techniques that have a high level of applicability to engineering, computer science, and industrial mathematics.
Lecture notes	Link to Moodle course: https://moodle-app2.let.ethz.ch/course/view.php?id=14316
Prerequisites / Notice	Solid knowledge of the C programming language, parallel programming paradigms such as OpenMP and MPI, and numerical methods in scientific computing in the area of linear algebra, mathematical optimization, and partial differential equations. The students might continue to study these HPC techniques within the annual USI-CSCS summer school on "Effective High-Performance Computing & Data Analytics Summer School". The content of the course is tailored for intermediate graduate students interested in both learning parallel programming models, and having hands-on experience using HPC systems. Starting from an introductory explanation of the available systems at CSCS, the course will progress to more applied topics such as parallel programming on accelerators, scientific libraries, and deep learning software frameworks. The following topics will be covered: GPU architectures, GPU programming, Message passing programming model (MPI), Performance optimization and scientific libraries, interactive supercomputing, Python libraries, Introduction to Machine Learning, and GPU optimized framework. The Summer School will be held from July 13 to 24, 2021 at the Steger Center in Riva San Vitale, located in the Italian area of Switzerland. More information about the summer school is available here: Link

Core Courses from Group II Recognition of 252-0220-00L Introduction to Machine Learning as a core course implies that this course unit cannot be recognised for the robotics field of specialisation.
Number	Title	Type	ECTS	Hours	Lecturers
252-0232-00L	Software Engineering	W	6 credits	2V + 1U	F. Friedrich Wicker, M. Schwerhoff
Abstract	This course introduces both theoretical and applied aspects of software engineering. It covers: - Software Architecture - Informal and formal Modeling - Design Patterns - Software Engineering Principles - Code Refactoring - Program Testing
Objective	The course has two main objectives: - Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software. - Be able to apply these techniques in practice.
Content	While the lecture will provide the theoretical foundations for the various aspects of software engineering, the students will apply those techniques in project work that will span over the whole semester - involving all aspects of software engineering, from understanding requirements over design and implementation to deployment and change requests.
Lecture notes	no lecture notes
Literature	Will be announced in the lecture
252-0220-00L	Introduction to Machine Learning Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch	W	8 credits	4V + 2U + 1A	A. Krause, F. Yang
Abstract	The course introduces the foundations of learning and making predictions based on data.
Objective	The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Content	- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)
Literature	Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Prerequisites / Notice	Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning"

Bachelor's Thesis If you wish to have recognised 402-2000-00L Scientific Works in Physics instead of 401-2000-00L Scientific Works in Mathematics (as allowed for the CSE programme), take contact with the Study Administration Office (www.math.ethz.ch/studiensekretariat) after having passed the performance assessment.
Number	Title	Type	ECTS	Hours	Lecturers
401-2000-00L	Scientific Works in Mathematics Target audience: Third year Bachelor students; Master students who cannot document to have received an adequate training in working scientifically.	O	0 credits		M. Burger
Abstract	Introduction to scientific writing for students with focus on publication standards and ethical issues, especially in the case of citations (references to works of others.)
Objective	Learn the basic standards of scientific works in mathematics.
Content	- Types of mathematical works - Publication standards in pure and applied mathematics - Data handling - Ethical issues - Citation guidelines
Lecture notes	Moodle of the Mathematics Library: https://moodle-app2.let.ethz.ch/course/view.php?id=519
Prerequisites / Notice	Directive Link
401-2000-01L	Lunch Sessions – Thesis Basics for Mathematics Students Details and registration for the optional MathBib training course: https://www.math.ethz.ch/mathbib-schulungen	Z	0 credits		Speakers
Abstract	Optional course "Recherchieren in der Mathematik" (held in German) by the Mathematics Library.
Objective
402-2000-00L	Scientific Works in Physics Target audience: Master students who cannot document to have received an adequate training in working scientifically. Directive Link	W	0 credits		C. Eichler
Abstract	Literature Review: ETH-Library, Journals in Physics, Google Scholar; Thesis Structure: The IMRAD Model; Document Processing: LaTeX and BibTeX, Mathematical Writing, AVETH Survival Guide; ETH Guidelines for Integrity; Authorship Guidelines; ETH Citation Etiquettes; Declaration of Originality.
Objective	Basic standards for scientific works in physics: How to write a Master Thesis. What to know about research integrity.
401-3990-18L	Bachelor's Thesis Only for Computational Science and Engineering BSc, Programme Regulations 2018. Successful participation in the course unit 401-2000-00L Scientific Works in Mathematics or 402-2000-00L Scientific Works in Physicsis is required. For more information, see www.math.ethz.ch/intranet/students/study-administration/theses.html	O	14 credits	30D	Supervisors
Abstract	The BSc thesis concludes the curriculum. In their BSc thesis, students should demonstrate their ability to carry out independent, structured scientific work. The purpose of the BSc thesis is to deepen knowledge in a certain subject and to bring students into closer contact with applications in an existing computational group. The BSc thesis requires approximately 420 hours of work.
Objective	In their Bsc thesis students should demonstrate their ability to carry out independent, structured scientific work. The purpose is to deepen knowledge in a certain subject and to enable students to collaborate in an existing scientific group to take a computational approach to problems encountered in applications.
Prerequisites / Notice	The supervisor responsible for the Bachelor thesis defines the task and determines the start and the submission date. The Bachelor thesis concludes with a written report. The Bachelor thesis is graded.

Bachelor Studies (Programme Regulations 2016)
Basic Courses
Block G3 227-0014-10L Operating Systems and Networks was offered for the last time in the Spring Semester 2019.
Number	Title	Type	ECTS	Hours	Lecturers
401-0674-00L	Numerical Methods for Partial Differential Equations Not meant for BSc/MSc students of mathematics.	O	10 credits	2G + 2U + 2P + 4A	R. Hiptmair
Abstract	Derivation, properties, and implementation of fundamental numerical methods for a few key partial differential equations: convection-diffusion, heat equation, wave equation, conservation laws. Implementation in C++ based on a finite element library.
Objective	Main skills to be acquired in this course: * Ability to implement fundamental numerical methods for the solution of partial differential equations efficiently. * Ability to modify and adapt numerical algorithms guided by awareness of their mathematical foundations. * Ability to select and assess numerical methods in light of the predictions of theory * Ability to identify features of a PDE (= partial differential equation) based model that are relevant for the selection and performance of a numerical algorithm. * Ability to understand research publications on theoretical and practical aspects of numerical methods for partial differential equations. * Skills in the efficient implementation of finite element methods on unstructured meshes. This course is neither a course on the mathematical foundations and numerical analysis of methods nor an course that merely teaches recipes and how to apply software packages.
Content	1 Second-Order Scalar Elliptic Boundary Value Problems 1.2 Equilibrium Models: Examples 1.3 Sobolev spaces 1.4 Linear Variational Problems 1.5 Equilibrium Models: Boundary Value Problems 1.6 Diffusion Models (Stationary Heat Conduction) 1.7 Boundary Conditions 1.8 Second-Order Elliptic Variational Problems 1.9 Essential and Natural Boundary Conditions 2 Finite Element Methods (FEM) 2.2 Principles of Galerkin Discretization 2.3 Case Study: Linear FEM for Two-Point Boundary Value Problems 2.4 Case Study: Triangular Linear FEM in Two Dimensions 2.5 Building Blocks of General Finite Element Methods 2.6 Lagrangian Finite Element Methods 2.7 Implementation of Finite Element Methods 2.7.1 Mesh Generation and Mesh File Format 2.7.2 Mesh Information and Mesh Data Structures 2.7.2.1 L EHR FEM++ Mesh: Container Layer 2.7.2.2 L EHR FEM++ Mesh: Topology Layer 2.7.2.3 L EHR FEM++ Mesh: Geometry Layer 2.7.3 Vectors and Matrices 2.7.4 Assembly Algorithms 2.7.4.1 Assembly: Localization 2.7.4.2 Assembly: Index Mappings 2.7.4.3 Distribute Assembly Schemes 2.7.4.4 Assembly: Linear Algebra Perspective 2.7.5 Local Computations 2.7.5.1 Analytic Formulas for Entries of Element Matrices 2.7.5.2 Local Quadrature 2.7.6 Treatment of Essential Boundary Conditions 2.8 Parametric Finite Element Methods 3 FEM: Convergence and Accuracy 3.1 Abstract Galerkin Error Estimates 3.2 Empirical (Asymptotic) Convergence of Lagrangian FEM 3.3 A Priori (Asymptotic) Finite Element Error Estimates 3.4 Elliptic Regularity Theory 3.5 Variational Crimes 3.6 FEM: Duality Techniques for Error Estimation 3.7 Discrete Maximum Principle 3.8 Validation and Debugging of Finite Element Codes 4 Beyond FEM: Alternative Discretizations [dropped] 5 Non-Linear Elliptic Boundary Value Problems [dropped] 6 Second-Order Linear Evolution Problems 6.1 Time-Dependent Boundary Value Problems 6.2 Parabolic Initial-Boundary Value Problems 6.3 Linear Wave Equations 7 Convection-Diffusion Problems [dropped] 8 Numerical Methods for Conservation Laws 8.1 Conservation Laws: Examples 8.2 Scalar Conservation Laws in 1D 8.3 Conservative Finite Volume (FV) Discretization 8.4 Timestepping for Finite-Volume Methods 8.5 Higher-Order Conservative Finite-Volume Schemes
Lecture notes	The lecture will be taught in flipped classroom format: - Video tutorials for all thematic units will be published online. - Tablet notes accompanying the videos will be made available to the audience as PDF. - A comprehensive lecture document will cover all aspects of the course.
Literature	Chapters of the following books provide supplementary reading (detailed references in course material): * D. Braess: Finite Elemente, Theorie, schnelle Löser und Anwendungen in der Elastizitätstheorie, Springer 2007 (available online). * S. Brenner and R. Scott. Mathematical theory of finite element methods, Springer 2008 (available online). * A. Ern and J.-L. Guermond. Theory and Practice of Finite Elements, volume 159 of Applied Mathematical Sciences. Springer, New York, 2004. * Ch. Großmann and H.-G. Roos: Numerical Treatment of Partial Differential Equations, Springer 2007. * W. Hackbusch. Elliptic Differential Equations. Theory and Numerical Treatment, volume 18 of Springer Series in Computational Mathematics. Springer, Berlin, 1992. * P. Knabner and L. Angermann. Numerical Methods for Elliptic and Parabolic Partial Differential Equations, volume 44 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * S. Larsson and V. Thomée. Partial Differential Equations with Numerical Methods, volume 45 of Texts in Applied Mathematics. Springer, Heidelberg, 2003. * R. LeVeque. Finite Volume Methods for Hyperbolic Problems. Cambridge Texts in Applied Mathematics. Cambridge University Press, Cambridge, UK, 2002. However, study of supplementary literature is not important for for following the course.
Prerequisites / Notice	Mastery of basic calculus and linear algebra is taken for granted. Familiarity with fundamental numerical methods (solution methods for linear systems of equations, interpolation, approximation, numerical quadrature, numerical integration of ODEs) is essential. Important: Coding skills and experience in C++ are essential. Homework assignments involve substantial coding, partly based on a C++ finite element library. The written examination will be computer based and will comprise coding tasks.
529-0431-00L	Physical Chemistry III: Molecular Quantum Mechanics	O	4 credits	4G	F. Merkt
Abstract	Postulates of quantum mechanics, operator algebra, Schrödinger's equation, state functions and expectation values, matrix representation of operators, particle in a box, tunneling, harmonic oscillator, molecular vibrations, angular momentum and spin, generalised Pauli principle, perturbation theory, electronic structure of atoms and molecules, Born-Oppenheimer approximation.
Objective	This is an introductory course in quantum mechanics. The course starts with an overview of the fundamental concepts of quantum mechanics and introduces the mathematical formalism. The postulates and theorems of quantum mechanics are discussed in the context of experimental and numerical determination of physical quantities. The course develops the tools necessary for the understanding and calculation of elementary quantum phenomena in atoms and molecules.
Content	Postulates and theorems of quantum mechanics: operator algebra, Schrödinger's equation, state functions and expectation values. Linear motions: free particles, particle in a box, quantum mechanical tunneling, the harmonic oscillator and molecular vibrations. Angular momentum: electronic spin and orbital motion, molecular rotations. Electronic structure of atoms and molecules: the Pauli principle, angular momentum coupling, the Born-Oppenheimer approximation. Variational principle and perturbation theory. Discussion of bigger systems (solids, nano-structures).
Lecture notes	A script written in German will be available. The script is, however, no replacement for personal notes during the lecture and does not cover all aspects discussed.

Block G4 Students that enrol for the second year in the CSE Bachelor Programme and whose first year examination did not involve the subject "Physics I" will instead of "Physics II" (402-0034-10L) take the "Physics I and II" (402-0043-00L and 402-0044-00L) courses with performance assessment as a yearly course. As of FS 2018 the course unit 151-0122-00L Fluid Dynamics for CSE gets replaced in Block G4 by 151-0102-00L Fluid Dynamics I.
Number	Title	Type	ECTS	Hours	Lecturers
402-0034-10L	Physics II	W	4 credits	2V + 2U	W. Wegscheider
Abstract	This is a two-semester course introducing students into the foundations of Modern Physics. Topics include electricity and magnetism, light, waves, quantum physics, solid state physics, and semiconductors. Selected topics with important applications in industry will also be considered.
Objective	The lecture is intended to promote critical, scientific thinking. Key concepts of Physics will be acquired, with a focus on technically relevant applications. At the end of the two semesters, students will have a good overview over the topics of classical and modern Physics.
Content	Introduction into Quantum Physics, Absorption and Emission of Electromagnetic Radiation, Basics of Solid State Physics, Semiconductors
Lecture notes	Lecture notes will be available in German.
Literature	Paul A. Tipler, Gene Mosca, Michael Basler und Renate Dohmen Physik: für Wissenschaftler und Ingenieure Spektrum Akademischer Verlag, 2009, 1636 Seiten, ca. 80 Euro. Paul A. Tipler, Ralph A. Llewellyn Moderne Physik Oldenbourg Wissenschaftsverlag, 2009, 982 Seiten, ca. 75 Euro.
Prerequisites / Notice	No testat requirements for this lecture.
402-0044-00L	Physics II	W	4 credits	3V + 1U	T. Esslinger
Abstract	Introduction to the concepts and tools in physics with the help of demonstration experiments: electromagnetism, optics, introduction to modern physics.
Objective	The concepts and tools in physics, as well as the methods of an experimental science are taught. The student should learn to identify, communicate and solve physical problems in his/her own field of science.
Content	Electromagnetism (electric current, magnetic fields, electromagnetic induction, magnetic materials, Maxwell's equations) Optics (light, geometrical optics, interference and diffraction) Short introduction to quantum physics
Lecture notes	The lecture follows the book "Physik" by Paul A. Tipler.
Literature	Paul A. Tipler and Gene Mosca Physik Springer Spektrum Verlag
151-0102-00L	Fluid Dynamics I	O	6 credits	4V + 2U	T. Rösgen
Abstract	An introduction to the physical and mathematical foundations of fluid dynamics is given. Topics include dimensional analysis, integral and differential conservation laws, inviscid and viscous flows, Navier-Stokes equations, boundary layers, turbulent pipe flow. Elementary solutions and examples are presented.
Objective	An introduction to the physical and mathematical principles of fluid dynamics. Fundamental terminology/principles and their application to simple problems.
Content	Phenomena, applications, foundations dimensional analysis and similitude; kinematic description; conservation laws (mass, momentum, energy), integral and differential formulation; inviscid flows: Euler equations, stream filament theory, Bernoulli equation; viscous flows: Navier-Stokes equations; boundary layers; turbulence
Lecture notes	Lecture notes (extended formulary) for the course are made available electronically.
Literature	Recommended book: Fluid Mechanics, Kundu & Cohen & Dowling, 6th ed., Academic Press / Elsevier (2015).
Prerequisites / Notice	Voraussetzungen: Physik, Analysis
529-0483-00L	Statistical Physics and Computer Simulation	O	4 credits	2V + 1U	S. Riniker, P. H. Hünenberger
Abstract	Principles and applications of statistical mechanics and equilibrium molecular dynamics, Monte Carlo simulation, Stochastic dynamics. Exercises using a MD simulation program to generate ensembles and subsequently calculate ensemble averages.
Objective	Introduction to statistical mechanics with the aid of computer simulation, development of skills to carry out statistical mechanical calculations using computers and interpret the results.
Content	Principles and applications of statistical mechanics and equilibrium molecular dynamics, Monte Carlo simulation, stochastic dynamics, free energy calculation. Exercises using a MD simulation program to generate ensembles and subsequently calculate ensemble averages.
Literature	will be announced in the lecture
Prerequisites / Notice	Since the exercises on the computer do convey and test essentially different skills as those being conveyed during the lectures and tested at the written exam, the results of a small programming project will be presented in a 10-minutes talk by pairs of students who had been working on the project. Additional information will be provided in the first lecture.

Core Courses
Number	Title	Type	ECTS	Hours	Lecturers
151-0116-00L	High Performance Computing for Science and Engineering (HPCSE) for CSE	O	7 credits	4G + 2P	P. Koumoutsakos, S. M. Martin
Abstract	This course focuses on programming methods and tools for parallel computing on multi and many-core architectures. Emphasis will be placed on practical and computational aspects of Bayesian Uncertainty Quantification and Machine Learning including the implementation of these algorithms on HPC architectures.
Objective	The course will teach - programming models and tools for multi and many-core architectures - fundamental concepts of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. - fundamentals of Deep Learning
Content	High Performance Computing: - Advanced topics in shared-memory programming - Advanced topics in MPI - GPU architectures and CUDA programming Uncertainty Quantification: - Uncertainty quantification under parametric and non-parametric modeling uncertainty - Bayesian inference with model class assessment - Markov Chain Monte Carlo simulation Machine Learning - Deep Neural Networks and Stochastic Gradient Descent - Deep Neural Networks for Data Compression (Autoencoders) - Recurrent Neural Networks
Lecture notes	https://www.cse-lab.ethz.ch/teaching/hpcse-ii_fs21/ Class notes, handouts
Literature	- Class notes - Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein - CUDA by example, J. Sanders and E. Kandrot - Data Analysis: A Bayesian Tutorial, D. Sivia and J. Skilling - An introduction to Bayesian Analysis - Theory and Methods, J. Gosh, N. Delampady and S. Tapas - Bayesian Data Analysis, A. Gelman, J. Carlin, H. Stern, D. Dunson, A. Vehtari and D. Rubin - Machine Learning: A Bayesian and Optimization Perspective, S. Theodorides
Prerequisites / Notice	Attendance of HPCSE I
252-0232-00L	Software Engineering	O	6 credits	2V + 1U	F. Friedrich Wicker, M. Schwerhoff
Abstract	This course introduces both theoretical and applied aspects of software engineering. It covers: - Software Architecture - Informal and formal Modeling - Design Patterns - Software Engineering Principles - Code Refactoring - Program Testing
Objective	The course has two main objectives: - Obtain an end-to-end (both, theoretical and practical) understanding of the core techniques used for building quality software. - Be able to apply these techniques in practice.
Content	While the lecture will provide the theoretical foundations for the various aspects of software engineering, the students will apply those techniques in project work that will span over the whole semester - involving all aspects of software engineering, from understanding requirements over design and implementation to deployment and change requests.
Lecture notes	no lecture notes
Literature	Will be announced in the lecture

Bachelor's Thesis If you wish to have recognised 402-2000-00L Scientific Works in Physics instead of 401-2000-00L Scientific Works in Mathematics (as allowed for the CSE programme), take contact with the Study Administration Office (www.math.ethz.ch/studiensekretariat) after having passed the performance assessment.
Number	Title	Type	ECTS	Hours	Lecturers
401-2000-00L	Scientific Works in Mathematics Target audience: Third year Bachelor students; Master students who cannot document to have received an adequate training in working scientifically.	O	0 credits		M. Burger
Abstract	Introduction to scientific writing for students with focus on publication standards and ethical issues, especially in the case of citations (references to works of others.)
Objective	Learn the basic standards of scientific works in mathematics.
Content	- Types of mathematical works - Publication standards in pure and applied mathematics - Data handling - Ethical issues - Citation guidelines
Lecture notes	Moodle of the Mathematics Library: https://moodle-app2.let.ethz.ch/course/view.php?id=519
Prerequisites / Notice	Directive Link
401-2000-01L	Lunch Sessions – Thesis Basics for Mathematics Students Details and registration for the optional MathBib training course: https://www.math.ethz.ch/mathbib-schulungen	Z	0 credits		Speakers
Abstract	Optional course "Recherchieren in der Mathematik" (held in German) by the Mathematics Library.
Objective
402-2000-00L	Scientific Works in Physics Target audience: Master students who cannot document to have received an adequate training in working scientifically. Directive Link	W	0 credits		C. Eichler
Abstract	Literature Review: ETH-Library, Journals in Physics, Google Scholar; Thesis Structure: The IMRAD Model; Document Processing: LaTeX and BibTeX, Mathematical Writing, AVETH Survival Guide; ETH Guidelines for Integrity; Authorship Guidelines; ETH Citation Etiquettes; Declaration of Originality.
Objective	Basic standards for scientific works in physics: How to write a Master Thesis. What to know about research integrity.
401-3990-01L	Bachelor's Thesis Only for Computational Science and Engineering BSc, Programme Regulations 2012 and 2016. Successful participation in the course unit 401-2000-00L Scientific Works in Mathematics or 402-2000-00L Scientific Works in Physicsis is required. For more information, see www.math.ethz.ch/intranet/students/study-administration/theses.html	O	8 credits	11D	Supervisors
Abstract	The BSc thesis concludes the curriculum. In their BSc thesis, students should demonstrate their ability to carry out independent, structured scientific work. The purpose of the BSc thesis is to deepen knowledge in a certain subject and to bring students into closer contact with applications in an existing computational group. The BSc thesis requires approximately 160 hours of work.
Objective	In their Bsc thesis students should demonstrate their ability to carry out independent, structured scientific work. The purpose is to deepen knowledge in a certain subject and to enable students to collaborate in an existing scientific group to take a computational approach to problems encountered in applications.
Prerequisites / Notice	The supervisor responsible for the Bachelor thesis defines the task and determines the start and the submission date. The Bachelor thesis concludes with a written report. The Bachelor thesis is graded.

For All Programme Regulations
Fields of Specialization
Astrophysics
Number	Title	Type	ECTS	Hours	Lecturers
402-0394-00L	Theoretical Cosmology Special Students UZH must book the module AST513 directly at UZH.	W	10 credits	4V + 2U	L. M. Mayer, J. Yoo
Abstract	This is the second of a two course series which starts with "General Relativity" and continues in the spring with "Theoretical Astrophysics and Cosmology", where the focus will be on applying general relativity to cosmology as well as developing the modern theory of structure formation in a cold dark matter Universe.
Objective	Learning the fundamentals of modern physical cosmology. This entails understanding the physical principles behind the description of the homogeneous Universe on large scales in the first part of the course, and moving on to the inhomogeneous Universe model where perturbation theory is used to study the development of structure through gravitational instability in the second part of the course. Modern notions of dark matter and dark energy will also be introduced and discussed.
Content	The course will cover the following topics: - Homogeneous cosmology - Thermal history of the universe, recombination, baryogenesis and nucleosynthesis - Dark matter and Dark Energy - Inflation - Perturbation theory: Relativistic and Newtonian - Model of structure formation and initial conditions from Inflation - Cosmic microwave background anisotropies - Spherical collapse and galaxy formation - Large scale structure and cosmological probes
Lecture notes	In 2021, the lectures will be live-streamed online at ETH from the Room HPV G5 at the lecture hours. The recordings will be available at the ETH website. The detailed information will be provided by the course website and the SLACK channel.
Literature	Suggested textbooks: H.Mo, F. Van den Bosch, S. White: Galaxy Formation and Evolution S. Carroll: Space-Time and Geometry: An Introduction to General Relativity S. Dodelson: Modern Cosmology Secondary textbooks: S. Weinberg: Gravitation and Cosmology V. Mukhanov: Physical Foundations of Cosmology E. W. Kolb and M. S. Turner: The Early Universe N. Straumann: General relativity with applications to astrophysics A. Liddle and D. Lyth: Cosmological Inflation and Large Scale Structure
Prerequisites / Notice	Knowledge of General Relativity is recommended.

Physics of the Atmosphere
Number	Title	Type	ECTS	Hours	Lecturers
701-1216-00L	Numerical Modelling of Weather and Climate	W	4 credits	3G	C. Schär, J. Vergara Temprado, M. Wild
Abstract	The course provides an introduction to weather and climate models. It discusses how these models are built addressing both the dynamical core and the physical parameterizations, and it provides an overview of how these models are used in numerical weather prediction and climate research. As a tutorial, students conduct a term project and build a simple atmospheric model using the language PYTHON.
Objective	At the end of this course, students understand how weather and climate models are formulated from the governing physical principles, and how they are used for climate and weather prediction purposes.
Content	The course provides an introduction into the following themes: numerical methods (finite differences and spectral methods); adiabatic formulation of atmospheric models (vertical coordinates, hydrostatic approximation); parameterization of physical processes (e.g. clouds, convection, boundary layer, radiation); atmospheric data assimilation and weather prediction; predictability (chaos-theory, ensemble methods); climate models (coupled atmospheric, oceanic and biogeochemical models); climate prediction. Hands-on experience with simple models will be acquired in the tutorials.
Lecture notes	Slides and lecture notes will be made available at Link
Literature	List of literature will be provided.
Prerequisites / Notice	Prerequisites: to follow this course, you need some basic background in atmospheric science, numerical methods (e.g., "Numerische Methoden in der Umweltphysik", 701-0461-00L) as well as experience in programming. Previous experience with PYTHON is useful but not required.

Chemistry
Number	Title	Type	ECTS	Hours	Lecturers
529-0474-00L	Quantum Chemistry	W	6 credits	3G	M. Reiher, T. Weymuth
Abstract	Introduction into the basic concepts of electronic structure theory and into numerical methods of quantum chemistry. Exercise classes are designed to deepen the theory; practical case studies using quantum chemical software to provide a 'hands-on' expertise in applying these methods.
Objective	Nowadays, chemical research can be carried out in silico, an intellectual achievement for which Pople and Kohn have been awarded the Nobel prize of the year 1998. This lecture shows how that has been accomplished. It works out the many-particle theory of many-electron systems (atoms and molecules) and discusses its implementation into computer programs. A complete picture of quantum chemistry shall be provided that will allow students to carry out such calculations on molecules (for accompanying experimental work in the wet lab or as a basis for further study of the theory).
Content	Basic concepts of many-particle quantum mechanics. Derivation of the many-electron theory for atoms and molecules; starting with the harmonic approximation for the nuclear problem and with Hartree-Fock theory for the electronic problem to Moeller-Plesset perturbation theory and configuration interaction and to coupled cluster and multi-configurational approaches. Density functional theory. Case studies using quantum mechanical software.
Lecture notes	Hand-outs in German will be provided for each lecture (they are supplemented by (computer) examples that continuously illustrate how the theory works). All information regarding this course, including links to the online streaming, will be available on this web page: https://reiher.ethz.ch/courses-and-seminars/exercises/QC_2021.html
Literature	Textbooks on Quantum Chemistry: F.L. Pilar, Elementary Quantum Chemistry, Dover Publications I.N. Levine, Quantum Chemistry, Prentice Hall Hartree-Fock in basis set representation: A. Szabo and N. Ostlund, Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory, McGraw-Hill Textbooks on Computational Chemistry: F. Jensen, Introduction to Computational Chemistry, John Wiley & Sons C.J. Cramer, Essentials of Computational Chemistry, John Wiley & Sons
Prerequisites / Notice	Basic knowledge in quantum mechanics (e.g. through course physical chemistry III - quantum mechanics) required
227-0161-00L	Molecular and Materials Modelling	W	4 credits	2V + 2U	D. Passerone, C. Pignedoli
Abstract	The course introduces the basic techniques to interpret experiments with contemporary atomistic simulation, including force fields or ab initio based molecular dynamics and Monte Carlo. Structural and electronic properties will be simulated hands-on for realistic systems. The modern methods of "big data" analysis applied to the screening of chemical structures will be introduced with examples.
Objective	The ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, X-ray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment.
Content	-Classical force fields in molecular and condensed phase systems -Methods for finding stationary states in a potential energy surface -Monte Carlo techniques applied to nanoscience -Classical molecular dynamics: extracting quantities and relating to experimentally accessible properties -From molecular orbital theory to quantum chemistry: chemical reactions -Condensed phase systems: from periodicity to band structure -Larger scale systems and their electronic properties: density functional theory and its approximations -Advanced molecular dynamics: Correlation functions and extracting free energies -The use of Smooth Overlap of Atomic Positions (SOAP) descriptors in the evaluation of the (dis)similarity of crystalline, disordered and molecular compounds
Lecture notes	A script will be made available and complemented by literature references.
Literature	D. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002. M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990. C. J. Cramer, Essentials of Computational Chemistry. Theories and Models, Wiley 2004 G. L. Miessler, P. J. Fischer, and Donald A. Tarr, Inorganic Chemistry, Pearson 2014. K. Huang, Statistical Mechanics, Wiley, 1987. N. W. Ashcroft, N. D. Mermin, Solid State Physics, Saunders College 1976. E. Kaxiras, Atomic and Electronic Structure of Solids, Cambridge University Press 2010.

Fluid Dynamics
Number	Title	Type	ECTS	Hours	Lecturers
151-0208-00L	Computational Methods for Flow, Heat and Mass Transfer Problems	W	4 credits	4G	D. W. Meyer-Massetti
Abstract	Numerical methods for the solution of flow, heat & mass transfer problems are presented and illustrated by analytical & computer exercises.
Objective	Knowledge of and practical experience with discretization and solution methods for computational fluid dynamics and heat and mass transfer problems
Content	- Introduction with application examples, steps to a numerical solution - Classification of PDEs, application examples - Finite differences - Finite volumes - Method of weighted residuals, spectral methods, finite elements - Stability analysis, consistency, convergence - Numerical solution methods, linear solvers The learning materials are illustrated with practical examples.
Lecture notes	Slides to be completed during the lecture will be handed out.
Literature	References are provided during the lecture. Notes in close agreement with the lecture material are available (in German).
Prerequisites / Notice	Basic knowledge in fluid dynamics, thermodynamics and programming (lecture: "Models, Algorithms and Data: Introduction to Computing")

Systems and Control
Number	Title	Type	ECTS	Hours	Lecturers
227-0216-00L	Control Systems II	W	6 credits	4G	R. Smith
Abstract	Introduction to basic and advanced concepts of modern feedback control.
Objective	Introduction to basic and advanced concepts of modern feedback control.
Content	This course is designed as a direct continuation of the course "Regelsysteme" (Control Systems). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow the treatment of typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance, and controller implementation issues.
Lecture notes	The slides of the lecture are available to download.
Literature	Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / Notice	Prerequisites: Control Systems or equivalent
227-0046-10L	Signals and Systems II	W	4 credits	2V + 2U	J. Lygeros
Abstract	Continuous and discrete time linear system theory, state space methods, frequency domain methods, controllability, observability, stability.
Objective	Introduction to basic concepts of system theory.
Content	Modeling and classification of dynamical systems. Modeling of linear, time invariant systems by state equations. Solution of state equations by time domain and Laplace methods. Stability, controllability and observability analysis. Frequency domain description, Bode and Nyquist plots. Sampled data and discrete time systems. Advanced topics: Nonlinear systems, chaos, discrete event systems, hybrid systems.
Lecture notes	Copy of transparencies
Literature	Recommended: K.J. Astrom and R. Murray, "Feedback Systems: An Introduction for Scientists and Engineers", Princeton University Press 2009 http://www.cds.caltech.edu/~murray/amwiki/

Robotics
Number	Title	Type	ECTS	Hours	Lecturers
151-0854-00L	Autonomous Mobile Robots	W	5 credits	4G	R. Siegwart, M. Chli, N. Lawrance
Abstract	The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation. Theory will be deepened by exercises with small mobile robots and discussed accross application examples.
Objective	The objective of this course is to provide the basics required to develop autonomous mobile robots and systems. Main emphasis is put on mobile robot locomotion and kinematics, environment perception, and probabilistic environment modeling, localizatoin, mapping and navigation.
Lecture notes	This lecture is enhanced by around 30 small videos introducing the core topics, and multiple-choice questions for continuous self-evaluation. It is developed along the TORQUE (Tiny, Open-with-Restrictions courses focused on QUality and Effectiveness) concept, which is ETH's response to the popular MOOC (Massive Open Online Course) concept.
Literature	This lecture is based on the Textbook: Introduction to Autonomous Mobile Robots Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, Second Edition 2011, ISBN: 978-0262015356
151-0566-00L	Recursive Estimation	W	4 credits	2V + 1U	R. D'Andrea
Abstract	Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way.
Objective	Learn the basic recursive estimation methods and their underlying principles.
Content	Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle.
Lecture notes	Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html
Prerequisites / Notice	Requirements: Introductory probability theory and matrix-vector algebra.
252-0579-00L	3D Vision	W	5 credits	3G + 1A	M. Pollefeys, V. Larsson
Abstract	The course covers camera models and calibration, feature tracking and matching, camera motion estimation via simultaneous localization and mapping (SLAM) and visual odometry (VO), epipolar and mult-view geometry, structure-from-motion, (multi-view) stereo, augmented reality, and image-based (re-)localization.
Objective	After attending this course, students will: 1. understand the core concepts for recovering 3D shape of objects and scenes from images and video. 2. be able to implement basic systems for vision-based robotics and simple virtual/augmented reality applications. 3. have a good overview over the current state-of-the art in 3D vision. 4. be able to critically analyze and asses current research in this area.
Content	The goal of this course is to teach the core techniques required for robotic and augmented reality applications: How to determine the motion of a camera and how to estimate the absolute position and orientation of a camera in the real world. This course will introduce the basic concepts of 3D Vision in the form of short lectures, followed by student presentations discussing the current state-of-the-art. The main focus of this course are student projects on 3D Vision topics, with an emphasis on robotic vision and virtual and augmented reality applications.
252-0220-00L	Introduction to Machine Learning Limited number of participants. Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact Prof. Krause for any questions in this regard. If necessary, please contact studiensekretariat@inf.ethz.ch	W	8 credits	4V + 2U + 1A	A. Krause, F. Yang
Abstract	The course introduces the foundations of learning and making predictions based on data.
Objective	The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.
Content	- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)
Literature	Textbook: Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
Prerequisites / Notice	Designed to provide a basis for following courses: - Advanced Machine Learning - Deep Learning - Probabilistic Artificial Intelligence - Seminar "Advanced Topics in Machine Learning"

Physics
Number	Title	Type	ECTS	Hours	Lecturers
402-0812-00L	Computational Statistical Physics	W	8 credits	2V + 2U	M. Krstic Marinkovic
Abstract	Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization.
Objective	The lecture will give a deeper insight into computer simulation methods in statistical physics. Thus, it is an ideal continuation of the lecture "Introduction to Computational Physics" of the autumn semester. In the first part students learn to apply the following methods: Classical Monte Carlo-simulations, finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Moreover, students learn about the application of statistical physics methods to Boltzmann machines and how to simulate non-equilibrium systems. In the second part, students apply molecular dynamics simulation methods. This part includes long range interactions, Ewald summation and discrete elements.
Content	Computer simulation methods in statistical physics. Classical Monte-Carlo-simulations: finite-size scaling, cluster algorithms, histogram-methods, renormalization group. Application to Boltzmann machines. Simulation of non-equilibrium systems. Molecular dynamics simulations: long range interactions, Ewald summation, discrete elements, parallelization.
Lecture notes	Lecture notes and slides are available online and will be distributed if desired.
Literature	Literature recommendations and references are included in the lecture notes.
Prerequisites / Notice	Some basic knowledge about statistical physics, classical mechanics and computational methods is recommended.
402-0810-00L	Computational Quantum Physics Special Students UZH must book the module PHY522 directly at UZH.	W	8 credits	2V + 2U	M. H. Fischer
Abstract	This course provides an introduction to simulation methods for quantum systems. Starting from the one-body problem, a special emphasis is on quantum many-body problems, where we cover both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, matrix product states, and quantum Monte Carlo methods).
Objective	Through lectures and practical programming exercises, after this course: Students are able to describe the difficulties of quantum mechanical simulations. Students are able to explain the strengths and weaknesses of the methods covered. Students are able to select an appropriate method for a given problem. Students are able to implement basic versions of all algorithms discussed.
Lecture notes	A script for this lecture will be provided.
Literature	A list of additional references will be provided in the script.
Prerequisites / Notice	A basic knowledge of quantum mechanics, numerical tools (numerical differentiation and integration, linear solvers, eigensolvers, root solvers, optimization), and a programming language (for the teaching assignments, you are free to choose your preferred one).
227-0161-00L	Molecular and Materials Modelling	W	4 credits	2V + 2U	D. Passerone, C. Pignedoli
Abstract	The course introduces the basic techniques to interpret experiments with contemporary atomistic simulation, including force fields or ab initio based molecular dynamics and Monte Carlo. Structural and electronic properties will be simulated hands-on for realistic systems. The modern methods of "big data" analysis applied to the screening of chemical structures will be introduced with examples.
Objective	The ability to select a suitable atomistic approach to model a nanoscale system, and to employ a simulation package to compute quantities providing a theoretically sound explanation of a given experiment. This includes knowledge of empirical force fields and insight in electronic structure theory, in particular density functional theory (DFT). Understanding the advantages of Monte Carlo and molecular dynamics (MD), and how these simulation methods can be used to compute various static and dynamic material properties. Basic understanding on how to simulate different spectroscopies (IR, X-ray, UV/VIS). Performing a basic computational experiment: interpreting the experimental input, choosing theory level and model approximations, performing the calculations, collecting and representing the results, discussing the comparison to the experiment.
Content	-Classical force fields in molecular and condensed phase systems -Methods for finding stationary states in a potential energy surface -Monte Carlo techniques applied to nanoscience -Classical molecular dynamics: extracting quantities and relating to experimentally accessible properties -From molecular orbital theory to quantum chemistry: chemical reactions -Condensed phase systems: from periodicity to band structure -Larger scale systems and their electronic properties: density functional theory and its approximations -Advanced molecular dynamics: Correlation functions and extracting free energies -The use of Smooth Overlap of Atomic Positions (SOAP) descriptors in the evaluation of the (dis)similarity of crystalline, disordered and molecular compounds
Lecture notes	A script will be made available and complemented by literature references.
Literature	D. Frenkel and B. Smit, Understanding Molecular Simulations, Academic Press, 2002. M. P. Allen and D.J. Tildesley, Computer Simulations of Liquids, Oxford University Press 1990. C. J. Cramer, Essentials of Computational Chemistry. Theories and Models, Wiley 2004 G. L. Miessler, P. J. Fischer, and Donald A. Tarr, Inorganic Chemistry, Pearson 2014. K. Huang, Statistical Mechanics, Wiley, 1987. N. W. Ashcroft, N. D. Mermin, Solid State Physics, Saunders College 1976. E. Kaxiras, Atomic and Electronic Structure of Solids, Cambridge University Press 2010.

Computational Finance Offered in the autumn semester.
Electromagnetics
Number	Title	Type	ECTS	Hours	Lecturers
227-0707-00L	Optimization Methods for Engineers	W	3 credits	2G	J. Smajic
Abstract	First half of the semester: Introduction to the main methods of numerical optimization with focus on stochastic methods such as genetic algorithms, evolutionary strategies, etc. Second half of the semester: Each participant implements a selected optimizer and applies it on a problem of practical interest.
Objective	Numerical optimization is of increasing importance for the development of devices and for the design of numerical methods. The students shall learn to select, improve, and combine appropriate procedures for efficiently solving practical problems.
Content	Typical optimization problems and their difficulties are outlined. Well-known deterministic search strategies, combinatorial minimization, and evolutionary algorithms are presented and compared. In engineering, optimization problems are often very complex. Therefore, new techniques based on the generalization and combination of known methods are discussed. To illustrate the procedure, various problems of practical interest are presented and solved with different optimization codes.
Lecture notes	PDF of a short skript (39 pages) plus the view graphs are provided
Prerequisites / Notice	Lecture only in the first half of the semester, exercises in form of small projects in the second half, presentation of the results in the last week of the semester.

Geophysics Recommended combinations: Subject 1 + Subject 2 Subject 1 + Subject 3 Subject 2 + Subject 3 Subject 3 + Subject 4 Subject 5 + Subject 6 + Subject 8 Subject 4 + Subject 5 Subject 7 + Subject 8
Geophysics: Subject 1 offered in the autumn semester
Geophysics: Subject 2 offered in the autumn semester
Geophysics: Subject 3
Number	Title	Type	ECTS	Hours	Lecturers
651-4008-00L	Dynamics of the Mantle and Lithosphere	W	3 credits	2G	A. Rozel
Abstract	The goal of this course is to obtain a detailed understanding of the physical properties, structure, and dynamical behavior of the mantle-lithosphere system, focusing mainly on Earth but also discussing how these processes occur differently in other terrestrial planets.
Objective	The goal of this course is to obtain a detailed understanding of the physical properties, structure, and dynamical behavior of the mantle-lithosphere system, focusing mainly on Earth but also discussing how these processes occur differently in other terrestrial planets.

Geophysics: Subject 4
Number	Title	Type	ECTS	Hours	Lecturers
651-4094-00L	Numerical Modelling for Applied Geophysics	W	5 credits	2G	J. Robertsson, H. Maurer
Abstract	Numerical modelling in environmental and exploration geophysics. The course covers different numerical methods such as finite difference and finite element methods applied to solve PDE’s for instance governing seismic wave propagation and geoelectric problems. Prerequisites include basic knowledge of (i) signal processing and applied mathematics such as Fourier analysis and (ii) Matlab.
Objective	After this course students should have a good overview of numerical modelling techniques commonly used in environmental and exploration geophysics. Students should be familiar with the basic principles of the methods and how they are used to solve real problems. They should know advantages and disadvantages as well as the limitations of the individual approaches. The course includes exercises in Matlab where the stduents both should lear, understand and use existing scripts as well as carrying out some coding in Matlab themselves.
Content	During the first part of the course, the following topics are covered: - Applications of modelling - Physics of acoustic, elastic, viscoelastic wave equations as well as Maxwell's equations for electromagnetic wave propagation and diffusive problems - Recap of basic techniques in signal processing and applied mathematics - Potential field modelling - Solving PDE's, boundary conditions and initial conditions - Acoustic/elastic wave propagation I, explicit time-domain finite-difference methods - Acoustic/elastic wave propagation II, Viscoelastic, pseudospectral - Acoustic/elastic wave propagation III, spectral accuracy in time, frequency domain FD, Eikonal - Implicit finite-difference methods (geoelectric) - Finite element methods, 1D/2D (heat equation) - Finite element methods, 3D (geoelectric) - Acoustic/elastic wave propagation IV, Finite element and spectral element methods - HPC and current challenges in computational seismology - Seismic data imaging project Most of the lecture modules are accompanied by exercises Small projects will be assigned to the students. They either include a programming exercise or applications of existing modelling codes.
Lecture notes	Presentation slides and some background material will be provided.
Literature	Igel, H., 2017. Computational seismology: a practical introduction. Oxford University Press.
Prerequisites / Notice	This course is offered as a full semester course. During the second part of the semester some lecture slots will be dedicated towards working on exercises and course projects.

Geophysics: Subject 5 offered in the autumn semester
Geophysics: Subject 6
Number	Title	Type	ECTS	Hours	Lecturers
651-4006-00L	Seismology of the Spherical Earth	W	3 credits	3G	M. van Driel, S. C. Stähler
Abstract	Brief review of continuum mechanics and the seismic wave equation; P and S waves; reciprocity and representation theorems; eikonal equation and ray tracing; Huygens and Fresnel; surface-waves; normal-modes; seismic interferometry and noise; numerical solutions.
Objective	After taking this course, students will have the background knowledge necessary to start an original research project in quantitative seismology.
Literature	Shearer, P., Introduction to Seismology, Cambridge University Press, 1999. Aki, K. and P. G. Richards, Quantitative Seismology, second edition, University Science Books, Sausalito, 2002. Nolet, G., A Breviary of Seismic Tomography, Cambridge University Press, 2008.
Prerequisites / Notice	This is a quantitative lecture with an emphasis on mathematical description of wave propagation phenomena on the global scale, hence basic knowledge in vector calculus, linear algebra and analysis as well as seismology (e.g. from the 'wave propagation' lecture) are essential to follow this course.

Geophysics: Subject 7
Number	Title	Type	ECTS	Hours	Lecturers
651-4096-00L	Inverse Theory I: Basics	W	3 credits	2V	A. Fichtner
Abstract	Inverse theory is the art of inferring properties of a physical system from noisy and sparse observations. It is used to transform observations of waves into 3D images of a medium seismic tomography, medical imaging and material science; to constrain density in the Earth from gravity; to obtain probabilities of life on exoplanets ... . Inverse theory is at the heart of many natural sciences.
Objective	The goal of this course is to enable students to develop a mathematical formulation of specific inference (inverse) problems that may arise anywhere in the physical sciences, and to implement suitable solution methods. Furthermore, students should become aware that nearly all relevant inverse problems are ill-posed, and that their meaningful solution requires the addition of prior knowledge in the form of expertise and physical intuition. This is what makes inverse theory an art.
Content	This first of two courses covers the basics needed to address (and hopefully solve) any kind of inverse problem. Starting from the description of information in terms of probabilities, we will derive Bayes' Theorem, which forms the mathematical foundation of modern scientific inference. This will allow us to formalise the process of gaining information about a physical system using new observations. Following the conceptual part of the course, we will focus on practical solutions of inverse problems, which will lead us to study Monte Carlo methods and the special case of least-squares inversion. In more detail, we aim to cover the following main topics: 1. The nature of observations and physical model parameters 2. Representing information by probabilities 3. Bayes' theorem and mathematical scientific inference 4. Random walks and Monte Carlo Methods 5. The Metropolis-Hastings algorithm 6. Simulated Annealing 7. Linear inverse problems and the least-squares method 8. Resolution and the nullspace 9. Basic concepts of iterative nonlinear inversion methods While the concepts introduced in this course are universal, they will be illustrated with numerous simple and intuitive examples. These will be complemented with a collection of computer and programming exercises. Prerequisites for this course include (i) basic knowledge of analysis and linear algebra, (ii) basic programming skills, for instance in Matlab or Python, and (iii) scientific curiosity.
Lecture notes	Presentation slides and detailed lecture notes will be provided.
Prerequisites / Notice	This course is offered as a half-semester course during the first part of the semester
651-4096-02L	Inverse Theory II: Applications Prerequisites: The successful completion of 651-4096-00L Inverse Theory I: Basics is mandatory.	W	3 credits	2G	A. Fichtner, C. Böhm
Abstract	This second part of the course on Inverse Theory provides an introduction to the numerical solution of large-scale inverse problems. Specific examples are drawn from different areas of geophysics and image processing. Students solve various model problems using python and jupyter notebooks, and familiarize themselves with relevant open-source libraries and commercial software.
Objective	This course provides numerical tools and recipes to solve (non)-linear inverse problems arising in nearly all fields of science and engineering. After successful completion of the class, the students will have a thorough understanding of suitable solution algorithms, common challenges and possible mitigations to infer parameters that govern large-scale physical systems from sparse data measurements. Prerequisites for this course are (i) 651-4096-00L Inverse Theory: Basics, (ii) basic programming skills.
Content	The class discusses several important concepts to solve (non)-linear inverse problems and demonstrates how to apply them to real-world data applications. All sessions are split into a lecture part in the first half, followed by tutorials using python and jupyter notebooks in the second. The range of covered topics include: 1. Regularization filters and image deblurring 2. Travel-time tomography 3. Line-search methods 4. Time reversal and Born’s approximation 5. Adjoint methods 6. Full-waveform inversion
Lecture notes	Presentation slides and some background material will be provided.
Prerequisites / Notice	This course is offered as a half-semester course during the second part of the semester

Geophysics: Subject 8 offered in the autumn semester
Biology
Number	Title	Type	ECTS	Hours	Lecturers
636-0702-00L	Statistical Models in Computational Biology	W	6 credits	2V + 1U + 2A	N. Beerenwinkel
Abstract	The course offers an introduction to graphical models and their application to complex biological systems. Graphical models combine a statistical methodology with efficient algorithms for inference in settings of high dimension and uncertainty. The unifying graphical model framework is developed and used to examine several classical and topical computational biology methods.
Objective	The goal of this course is to establish the common language of graphical models for applications in computational biology and to see this methodology at work for several real-world data sets.
Content	Graphical models are a marriage between probability theory and graph theory. They combine the notion of probabilities with efficient algorithms for inference among many random variables. Graphical models play an important role in computational biology, because they explicitly address two features that are inherent to biological systems: complexity and uncertainty. We will develop the basic theory and the common underlying formalism of graphical models and discuss several computational biology applications. Topics covered include conditional independence, Bayesian networks, Markov random fields, Gaussian graphical models, EM algorithm, junction tree algorithm, model selection, Dirichlet process mixture, causality, the pair hidden Markov model for sequence alignment, probabilistic phylogenetic models, phylo-HMMs, microarray experiments and gene regulatory networks, protein interaction networks, learning from perturbation experiments, time series data and dynamic Bayesian networks. Some of the biological applications will be explored in small data analysis problems as part of the exercises.
Lecture notes	no
Literature	- Airoldi EM (2007) Getting started in probabilistic graphical models. PLoS Comput Biol 3(12): e252. doi:10.1371/journal.pcbi.0030252 - Bishop CM. Pattern Recognition and Machine Learning. Springer, 2007. - Durbin R, Eddy S, Krogh A, Mitchinson G. Biological Sequence Analysis. Cambridge university Press, 2004

Electives In the ‘electives’ subcategory, at least two course units must be successfully completed.
Number	Title	Type	ECTS	Hours	Lecturers
151-3202-00L	Product Development and Engineering Design Number of participants limited to 60.	W	4 credits	2G	K. Shea, T. Stankovic
Abstract	The course introduces students to the product development process. In a team, you will explore the early phases of conceptual development and product design, from ideation and concept generation through to hands-on prototyping. This is an opportunity to gain product development experience and improve your skills in prototyping and presenting your product ideas. The project topic changes each year.
Objective	The course introduces you to the product development process and methods in engineering design for: product planning, user-centered design, creating product specifications, ideation including concept generation and selection methods, material selection methods and prototyping. Further topics include design for manufacture and design for additive manufacture. You will actively apply the process and methods learned throughout the semester in a team on a product development project including prototyping.
Content	Weekly topics accompanying the product development project include: 1 Introduction to Product Development and Engineering Design 2 Product Planning and Social-Economic-Technology (SET) Factors 3 User-Centered Design and Product Specifications 4 Concept Generation and Selection Methods 5 System Design and Embodiment Design 6 Prototyping and Prototype Planning 7 Material Selection in Engineering Design 8 Design for Manufacture and Design for Additive Manufacture
Lecture notes	available on Moodle
Literature	Ulrich, Eppinger, and Yang, Product Design and Development. 7th ed., McGraw-Hill Education, 2020. Cagan and Vogel, Creating Breakthrough Products: Revealing the Secrets that Drive Global Innovation, 2nd Edition, Pearson Education, 2013.
Prerequisites / Notice	Although the course is offered to ME (BSc and MSc) and CS (BSc and MSc) students, priority will be given to ME BSc students in the Focus Design, Mechanics, and Materials if the course is full.
151-0840-00L	Optimization and Machine Learning Note: previous course title until FS20 "Principles of FEM-Based Optimization and Robustness Analysis".	W	4 credits	2V + 2U	B. Berisha, D. Mohr
Abstract	The course teaches the basics of nonlinear optimization and concepts of machine learning. An introduction to the finite element method allows an extension of the application area to real engineering problems such as structural optimization and modeling of material behavior on different length scales.
Objective	Students will learn mathematical optimization methods including gradient based and gradient free methods as well as established algorithms in the context of machine learning to solve real engineering problems, which are generally non-linear in nature. Strategies to ensure efficient training of machine learning models based on large data sets define another teaching goal of the course. Optimization tools (MATLAB, LS-Opt, Python) and the finite element program ABAQUS are presented to solve both general and real engineering problems.
Content	- Introduction into Nonlinear Optimization - Design of Experiments DoE - Introduction into Nonlinear Finite Element Analysis - Optimization based on Meta Modeling Techniques - Shape and Topology Optimization - Robustness and Sensitivity Analysis - Fundamentals of Machine Learning - Generalized methods for regression and classification, Neural Networks, Support Vector machines - Supervised and unsupervised learning
Lecture notes	Lecture slides and literature
151-0206-00L	Energy Systems and Power Engineering	W	4 credits	2V + 2U	R. S. Abhari, A. Steinfeld
Abstract	Introductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing.
Objective	Introductory first course for the specialization in ENERGY. The course provides an overall view of the energy field and pertinent global problems, reviews some of the thermodynamic basics in energy conversion, and presents the state-of-the-art technology for power generation and fuel processing.
Content	World primary energy resources and use: fossil fuels, renewable energies, nuclear energy; present situation, trends, and future developments. Sustainable energy system and environmental impact of energy conversion and use: energy, economy and society. Electric power and the electricity economy worldwide and in Switzerland; production, consumption, alternatives. The electric power distribution system. Renewable energy and power: available techniques and their potential. Cost of electricity. Conventional power plants and their cycles; state-of-the-art and advanced cycles. Combined cycles and cogeneration; environmental benefits. Solar thermal; concentrated solar power; solar photovoltaics. Fuel cells: characteristics, fuel reforming and combined cycles.
Lecture notes	Vorlesungsunterlagen werden verteilt
151-0306-00L	Visualization, Simulation and Interaction - Virtual Reality I	W	4 credits	4G	A. Kunz
Abstract	Technology of Virtual Reality. Human factors, Creation of virtual worlds, Lighting models, Display- and acoustic- systems, Tracking, Haptic/tactile interaction, Motion platforms, Virtual prototypes, Data exchange, VR Complete systems, Augmented reality, Collaboration systems; VR and Design; Implementation of the VR in the industry; Human Computer Interfaces (HCI).
Objective	The product development process in the future will be characterized by the Digital Product which is the center point for concurrent engineering with teams spreas worldwide. Visualization and simulation of complex products including their physical behaviour at an early stage of development will be relevant in future. The lecture will give an overview to techniques for virtual reality, to their ability to visualize and to simulate objects. It will be shown how virtual reality is already used in the product development process. • Students are able to evaluate and select the most appropriate VR technology for a given task regarding: o Visualization technologies displays/projection systems/head-mounted displays o Tracking systems (inertia/optical/electromagnetic) o Interaction technologies (sensing gloves/real walking/eye tracking/touch/etc.) • Students are able to develop a VR application • Students are able to apply VR to industrial needs • Students will be able to apply the gained knowledge to a practical realization • Students will be able to compare different operation principles (VR/AR/MR/XR)
Content	Introduction to the world of virtual reality; development of new VR-techniques; introduction to 3D-computergraphics; modelling; physical based simulation; human factors; human interaction; equipment for virtual reality; display technologies; tracking systems; data gloves; interaction in virtual environment; navigation; collision detection; haptic and tactile interaction; rendering; VR-systems; VR-applications in industry, virtual mockup; data exchange, augmented reality.
Lecture notes	A complete version of the handout is also available in English.
Prerequisites / Notice	Voraussetzungen: keine Vorlesung geeignet für D-MAVT, D-ITET, D-MTEC und D-INF Testat/ Kredit-Bedingungen/ Prüfung: – Teilnahme an Vorlesung und Kolloquien – Erfolgreiche Durchführung von Übungen in Teams – Mündliche Einzelprüfung 30 Minuten
151-0314-00L	Information Technologies in the Digital Product	W	4 credits	3G	E. Zwicker, R. Montau
Abstract	Objectives, Concepts and Methods of Digitalization, Digital Product and Product Lifecycle Management (PLM), Industry 4.0 Concepts for Digitalization: Product Structures, Optimization of Engineering Processes with digital models in Sales, Production, Service, Digital Twin versus Digital Thread PLM Fundamentals: Objects, Structures, Processes, Integrations, Visualization Best Practices
Objective	Students learn the fundamentals and concepts of Digitalization along the in the product lifecycle on the foundation of Product Lifecycle Management (PLM) technologies, the usage of databases, the integration of CAx systems and Visualization/AR, the configuration of computer-based collaboration leveraging IT-standards as well as variant and configuration management to enable an efficient utilization of the digital product approach in industry 4.0.
Content	Possibilities and potential of modern IT applications focussing on PLM and CAx technologies for targeted utilization in the context of product platform - business processes - IT tools. Introduction to the concepts of Product Lifecycle Management (PLM): information modeling, data management, revision, usage and distribution of product data. Structure and functional principles of PLM systems. Integration of new IT technologies in business processes. Possibilities of publication and automatic configuration of product variants via the Internet. Using state-of-the-art information and communication technologies to develop products globally across distributed locations. Interfaces in computer-integrated product development. Selection, configuration, adaptation and introduction of PLM systems. Examples and case studies for industrial usage of modern information technologies. Learning modules: - Introduction to Digitalization (Digital Product, PLM technology) - Database technology (foundation of digitalization) - Object Management - Object Classification - Object identification with Part Numbering Systems - CAx/PLM integration with Visualization/AR - Workflow & Change Management - Interfaces of the Digital Product - Enterprise Application Integration (EAI)
Lecture notes	Didactic concept / learning materials: The course consists of lectures and exercises based on practical examples. Provision of lecture handouts and script digitally in Moodle.
Prerequisites / Notice	Prerequisites: None Recommended: Fokus-Project, interest in Digitalization Lecture appropriate for D-MAVT, D-MTEC, D-ITET and D-INFK Testat/Credit Requirements / Exam: - execution of exercises in teams (recommended) - Oral exam 30 minutes, based on concrete problem cases
151-0660-00L	Model Predictive Control	W	4 credits	2V + 1U	M. Zeilinger, A. Carron
Abstract	Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.
Objective	Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.
Content	- Review of required optimal control theory - Basics on optimization - Receding-horizon control (MPC) for constrained linear systems - Theoretical properties of MPC: Constraint satisfaction and stability - Computation: Explicit and online MPC - Practical issues: Tracking and offset-free control of constrained systems, soft constraints - Robust MPC: Robust constraint satisfaction - Nonlinear MPC: Theory and computation - Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization - Simulation-based project providing practical experience with MPC
Lecture notes	Script / lecture notes will be provided.
Prerequisites / Notice	One semester course on automatic control, Matlab, linear algebra. Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control. Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).
151-0940-00L	Modelling and Mathematical Methods in Process and Chemical Engineering	W	4 credits	3G	M. Mazzotti
Abstract	Study of the non-numerical solution of systems of ordinary differential equations and first order partial differential equations, with application to chemical kinetics, simple batch distillation, and chromatography.
Objective	Study of the non-numerical solution of systems of ordinary differential equations and first order partial differential equations, with application to chemical kinetics, simple batch distillation, and chromatography.
Content	Development of mathematical models in process and chemical engineering, particularly for chemical kinetics, batch distillation, and chromatography. Study of systems of ordinary differential equations (ODEs), their stability, and their qualitative analysis. Study of single first order partial differential equation (PDE) in space and time, using the method of characteristics. Application of the theory of ODEs to population dynamics, chemical kinetics (Belousov-Zhabotinsky reaction), and simple batch distillation (residue curve maps). Application of the method of characteristic to chromatography.
Lecture notes	no skript
Literature	A. Varma, M. Morbidelli, "Mathematical methods in chemical engineering," Oxford University Press (1997) H.K. Rhee, R. Aris, N.R. Amundson, "First-order partial differential equations. Vol. 1," Dover Publications, New York (1986) R. Aris, "Mathematical modeling: A chemical engineer’s perspective," Academic Press, San Diego (1999)
151-0980-00L	Biofluiddynamics	W	4 credits	2V + 1U	D. Obrist, P. Jenny
Abstract	Introduction to the fluid dynamics of the human body and the modeling of physiological flow processes (biomedical fluid dynamics).
Objective	A basic understanding of fluid dynamical processes in the human body. Knowledge of the basic concepts of fluid dynamics and the ability to apply these concepts appropriately.
Content	This lecture is an introduction to the fluid dynamics of the human body (biomedical fluid dynamics). For selected topics of human physiology, we introduce fundamental concepts of fluid dynamics (e.g., creeping flow, incompressible flow, flow in porous media, flow with particles, fluid-structure interaction) and use them to model physiological flow processes. The list of studied topics includes the cardiovascular system and related diseases, blood rheology, microcirculation, respiratory fluid dynamics and fluid dynamics of the inner ear.
Lecture notes	Lecture notes are provided electronically.
Literature	A list of books on selected topics of biofluiddynamics can be found on the course web page.
227-0052-10L	Electromagnetic Fields and Waves	W	4 credits	2V + 2U	L. Novotny
Abstract	This course is focused on the generation and propagation of electromagnetic fields. Based on Maxwell's equations we will derive the wave equation and its solutions. Specifically, we will discuss fields and waves in free space, refraction and reflection at plane interfaces, dipole radiation and Green functions, vector and scalar potentials, as well as gauge transformations.
Objective	Understanding of electromagnetic fields
227-0418-00L	Algebra and Error Correcting Codes	W	6 credits	4G	H.‑A. Loeliger
Abstract	The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.
Objective	The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.
Content	Error correcting codes: coding and modulation, linear codes, Hamming space codes, Euclidean space codes, trellises and Viterbi decoding, convolutional codes, factor graphs and message passing algorithms, low-density parity check codes, turbo codes, polar codes, Reed-Solomon codes. Algebra: groups, rings, homomorphisms, quotient groups, ideals, finite fields, vector spaces, polynomials.
Lecture notes	Lecture Notes (english)
227-0420-00L	Information Theory II	W	6 credits	4G	A. Lapidoth, S. M. Moser
Abstract	This course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.
Objective	The course's objective is to introduce the students to additional information measures and to equip them with the tools that are needed to conduct research in Information Theory as it relates to Communication Networks and to Statistics.
Content	Sanov's Theorem, Rényi entropy and guessing, differential entropy, maximum entropy, the Gaussian channel, the entropy-power inequality, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, the Gelfand-Pinsker problem, and Fisher information.
Lecture notes	n/a
Literature	T.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006
Prerequisites / Notice	Basic introductory course on Information Theory.
227-0104-00L	Communication and Detection Theory	W	6 credits	4G	A. Lapidoth
Abstract	This course teaches the foundations of modern digital communications and detection theory. Topics include the geometry of the space of energy-limited signals; the baseband representation of passband signals, spectral efficiency and the Nyquist Criterion; the power and power spectral density of PAM and QAM; hypothesis testing; Gaussian stochastic processes; and detection in white Gaussian noise.
Objective	This is an introductory class to the field of wired and wireless communication. It offers a glimpse at classical analog modulation (AM, FM), but mainly focuses on aspects of modern digital communication, including modulation schemes, spectral efficiency, power budget analysis, block and convolu- tional codes, receiver design, and multi- accessing schemes such as TDMA, FDMA and Spread Spectrum.
Content	- Baseband representation of passband signals. - Bandwidth and inner products in baseband and passband. - The geometry of the space of energy-limited signals. - The Sampling Theorem as an orthonormal expansion. - Sampling passband signals. - Pulse Amplitude Modulation (PAM): energy, power, and power spectral density. - Nyquist Pulses. - Quadrature Amplitude Modulation (QAM). - Hypothesis testing. - The Bhattacharyya Bound. - The multivariate Gaussian distribution - Gaussian stochastic processes. - Detection in white Gaussian noise.
Lecture notes	n/a
Literature	A. Lapidoth, A Foundation in Digital Communication, Cambridge University Press, 2nd edition (2017)
227-0120-00L	Communication Networks	W	6 credits	4G	L. Vanbever
Abstract	At the end of this course, you will understand the fundamental concepts behind communication networks and the Internet. Specifically, you will be able to: - understand how the Internet works; - build and operate Internet-like infrastructures; - identify the right set of metrics to evaluate the performance of a network and propose ways to improve it.
Objective	At the end of the course, the students will understand the fundamental concepts of communication networks and Internet-based communications. Specifically, students will be able to: - understand how the Internet works; - build and operate Internet-like network infrastructures; - identify the right set of metrics to evaluate the performance or the adequacy of a network and propose ways to improve it (if any). The course will introduce the relevant mechanisms used in today's networks both from an abstract perspective but also from a practical one by presenting many real-world examples and through multiple hands-on projects. For more information about the lecture, please visit: https://comm-net.ethz.ch
Lecture notes	Lecture notes and material for the course will be available before each course on: https://comm-net.ethz.ch
Literature	Most of course follows the textbook "Computer Networking: A Top-Down Approach (6th Edition)" by Kurose and Ross.
Prerequisites / Notice	No prior networking background is needed. The course will include some programming assignments (in Python) for which the material covered in Technische Informatik 1 (227-0013-00L) will be useful.
227-0159-00L	Semiconductor Devices: Quantum Transport at the Nanoscale	W	6 credits	2V + 2U	M. Luisier, A. Emboras
Abstract	This class offers an introduction into quantum transport theory, a rigorous approach to electron transport at the nanoscale. It covers different topics such as bandstructure, Wave Function and Non-equilibrium Green's Function formalisms, and electron interactions with their environment. Matlab exercises accompany the lectures where students learn how to develop their own transport simulator.
Objective	The continuous scaling of electronic devices has given rise to structures whose dimensions do not exceed a few atomic layers. At this size, electrons do not behave as particle any more, but as propagating waves and the classical representation of electron transport as the sum of drift-diffusion processes fails. The purpose of this class is to explore and understand the displacement of electrons through nanoscale device structures based on state-of-the-art quantum transport methods and to get familiar with the underlying equations by developing his own nanoelectronic device simulator.
Content	The following topics will be addressed: - Introduction to quantum transport modeling - Bandstructure representation and effective mass approximation - Open vs closed boundary conditions to the Schrödinger equation - Comparison of the Wave Function and Non-equilibrium Green's Function formalisms as solution to the Schrödinger equation - Self-consistent Schödinger-Poisson simulations - Quantum transport simulations of resonant tunneling diodes and quantum well nano-transistors - Top-of-the-barrier simulation approach to nano-transistor - Electron interactions with their environment (phonon, roughness, impurity,...) - Multi-band transport models
Lecture notes	Lecture slides are distributed every week and can be found at https://iis-students.ee.ethz.ch/lectures/quantum-transport-in-nanoscale-devices/
Literature	Recommended textbook: "Electronic Transport in Mesoscopic Systems", Supriyo Datta, Cambridge Studies in Semiconductor Physics and Microelectronic Engineering, 1997
Prerequisites / Notice	Basic knowledge of semiconductor device physics and quantum mechanics
227-0558-00L	Principles of Distributed Computing	W	7 credits	2V + 2U + 2A	R. Wattenhofer, M. Ghaffari
Abstract	We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.
Objective	Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week.
Content	Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds
Lecture notes	Available. Our course script is used at dozens of other universities around the world.
Literature	Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8
Prerequisites / Notice	Course pre-requisites: Interest in algorithmic problems. (No particular course needed.)
252-0211-00L	Information Security	W	8 credits	4V + 3U	D. Basin, S. Capkun
Abstract	This course provides an introduction to Information Security. The focus is on fundamental concepts and models, basic cryptography, protocols and system security, and privacy and data protection. While the emphasis is on foundations, case studies will be given that examine different realizations of these ideas in practice.
Objective	Master fundamental concepts in Information Security and their application to system building. (See objectives listed below for more details).
Content	1. Introduction and Motivation (OBJECTIVE: Broad conceptual overview of information security) Motivation: implications of IT on society/economy, Classical security problems, Approaches to defining security and security goals, Abstractions, assumptions, and trust, Risk management and the human factor, Course verview. 2. Foundations of Cryptography (OBJECTIVE: Understand basic cryptographic mechanisms and applications) Introduction, Basic concepts in cryptography: Overview, Types of Security, computational hardness, Abstraction of channel security properties, Symmetric encryption, Hash functions, Message authentication codes, Public-key distribution, Public-key cryptosystems, Digital signatures, Application case studies, Comparison of encryption at different layers, VPN, SSL, Digital payment systems, blind signatures, e-cash, Time stamping 3. Key Management and Public-key Infrastructures (OBJECTIVE: Understand the basic mechanisms relevant in an Internet context) Key management in distributed systems, Exact characterization of requirements, the role of trust, Public-key Certificates, Public-key Infrastructures, Digital evidence and non-repudiation, Application case studies, Kerberos, X.509, PGP. 4. Security Protocols (OBJECTIVE: Understand network-oriented security, i.e.. how to employ building blocks to secure applications in (open) networks) Introduction, Requirements/properties, Establishing shared secrets, Principal and message origin authentication, Environmental assumptions, Dolev-Yao intruder model and variants, Illustrative examples, Formal models and reasoning, Trace-based interleaving semantics, Inductive verification, or model-checking for falsification, Techniques for protocol design, Application case study 1: from Needham-Schroeder Shared-Key to Kerberos, Application case study 2: from DH to IKE. 5. Access Control and Security Policies (OBJECTIVES: Study system-oriented security, i.e., policies, models, and mechanisms) Motivation (relationship to CIA, relationship to Crypto) and examples Concepts: policies versus models versus mechanisms, DAC and MAC, Modeling formalism, Access Control Matrix Model, Roll Based Access Control, Bell-LaPadula, Harrison-Ruzzo-Ullmann, Information flow, Chinese Wall, Biba, Clark-Wilson, System mechanisms: Operating Systems, Hardware Security Features, Reference Monitors, File-system protection, Application case studies 6. Anonymity and Privacy (OBJECTIVE: examine protection goals beyond standard CIA and corresponding mechanisms) Motivation and Definitions, Privacy, policies and policy languages, mechanisms, problems, Anonymity: simple mechanisms (pseudonyms, proxies), Application case studies: mix networks and crowds. 7. Larger application case study: GSM, mobility
263-4660-00L	Applied Cryptography Number of participants limited to 150.	W	8 credits	3V + 2U + 2P	K. Paterson
Abstract	This course will introduce the basic primitives of cryptography, using rigorous syntax and game-based security definitions. The course will show how these primitives can be combined to build cryptographic protocols and systems.
Objective	The goal of the course is to put students' understanding of cryptography on sound foundations, to enable them to start to build well-designed cryptographic systems, and to expose them to some of the pitfalls that arise when doing so.
Content	Basic symmetric primitives (block ciphers, modes, hash functions); generic composition; AEAD; basic secure channels; basic public key primitives (encryption,signature, DH key exchange); ECC; randomness; applications.
Literature	Textbook: Boneh and Shoup, “A Graduate Course in Applied Cryptography”, https://crypto.stanford.edu/~dabo/cryptobook/BonehShoup_0_4.pdf.
Prerequisites / Notice	Students should have taken the D-INFK Bachelor's course “Information Security" (252-0211-00) or an alternative first course covering cryptography at a similar level. / In this course, we will use Moodle for content delivery: https://moodle-app2.let.ethz.ch/course/view.php?id=14558.
252-0570-00L	Game Programming Laboratory In the Master Programme max. 10 credits can be accounted by Labs on top of the Interfocus Courses. Additional Labs will be listed on the Addendum.	W	10 credits	9P	B. Sumner
Abstract	The goal of this course is the in-depth understanding of the technology and programming underlying computer games. Students gradually design and develop a computer game in small groups and get acquainted with the art of game programming.
Objective	The goal of this new course is to acquaint students with the technology and art of programming modern three-dimensional computer games.
Content	This course addresses modern three-dimensional computer game technology. During the course, small groups of students will design and develop a computer game. Focus will be put on technical aspects of game development, such as rendering, cinematography, interaction, physics, animation, and AI. In addition, we will cultivate creative thinking for advanced gameplay and visual effects. The "laboratory" format involves a practical, hands-on approach with traditional lectures. We will meet once a week to discuss technical issues and to track progress. For development we use MonoGames, which is a collection of libraries and tools that facilitate game development. While development will take place on PCs, we will ultimately deployour games on the Xbox One console. At the end of the course we will present our results to the public.
Lecture notes	Game Design Workshop: A Playcentric Approach to Creating Innovative Games by Tracy Fullerton
Prerequisites / Notice	The number of participants is limited. Prerequisites include: - Good programming skills (Java, C++, C#, etc.) - CG experience: Students should have taken, at a minimum, Visual Computing. Higher level courses are recommended, such as Introduction to Computer Graphics, Surface Representations and Geometric Modeling, and Physically-based Simulation in Computer Graphics.
252-0538-00L	Shape Modeling and Geometry Processing	W	8 credits	2V + 1U + 4A	O. Sorkine Hornung
Abstract	This course covers the fundamentals and some of the latest developments in geometric modeling and geometry processing. Topics include surface modeling based on point clouds and polygonal meshes, mesh generation, surface reconstruction, mesh fairing and parameterization, discrete differential geometry, interactive shape editing, topics in digital shape fabrication.
Objective	The students will learn how to design, program and analyze algorithms and systems for interactive 3D shape modeling and geometry processing.
Content	Recent advances in 3D geometry processing have created a plenitude of novel concepts for the mathematical representation and interactive manipulation of geometric models. This course covers the fundamentals and some of the latest developments in geometric modeling and geometry processing. Topics include surface modeling based on point clouds and triangle meshes, mesh generation, surface reconstruction, mesh fairing and parameterization, discrete differential geometry, interactive shape editing and digital shape fabrication.
Lecture notes	Slides and course notes
Prerequisites / Notice	Prerequisites: Visual Computing, Computer Graphics or an equivalent class. Experience with C++ programming. Solid background in linear algebra and analysis. Some knowledge of differential geometry, computational geometry and numerical methods is helpful but not a strict requirement.
263-5806-00L	Computational Models of Motion	W	8 credits	2V + 2U + 3A	S. Coros, M. Bächer, B. Thomaszewski
Abstract	This course covers fundamentals of physics-based modelling and numerical optimization from the perspective of character animation and robotics applications. The methods discussed in class derive their theoretical underpinnings from applied mathematics, control theory and computational mechanics, and they will be richly illustrated using examples ranging from locomotion controllers and crowd simula
Objective	Students will learn how to represent, model and algorithmically control the behavior of animated characters and real-life robots. The lectures are accompanied by programming assignments (written in C++) and a capstone project.
Content	Optimal control and trajectory optimization; multibody systems; kinematics; forward and inverse dynamics; constrained and unconstrained numerical optimization; mass-spring models for crowd simulation; FEM; compliant systems; sim-to-real; robotic manipulation of elastically-deforming objects.
Prerequisites / Notice	Experience with C++ programming, numerical linear algebra and multivariate calculus. Some background in physics-based modeling, kinematics and dynamics is helpful, but not necessary.
252-3900-00L	Big Data for Engineers This course is not intended for Computer Science and Data Science MSc students!	W	6 credits	2V + 2U + 1A	G. Fourny
Abstract	This course is part of the series of database lectures offered to all ETH departments, together with Information Systems for Engineers. It introduces the most recent advances in the database field: how do we scale storage and querying to Petabytes of data, with trillions of records? How do we deal with heterogeneous data sets? How do we deal with alternate data shapes like trees and graphs?
Objective	This lesson is complementary with Information Systems for Engineers as they cover different time periods of database history and practices -- you can even take both lectures at the same time. The key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations. This combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm". Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small. The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof. After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently.
Content	This course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. It targets specifically students with a scientific or Engineering, but not Computer Science, background. We take the monolithic, one-machine relational stack from the 1970s, smash it down and rebuild it on top of large clusters: starting with distributed storage, and all the way up to syntax, models, validation, processing, indexing, and querying. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem. No data is harmed during this course, however, please be psychologically prepared that our data may not always be in normal form. - physical storage: distributed file systems (HDFS), object storage(S3), key-value stores - logical storage: document stores (MongoDB), column stores (HBase) - data formats and syntaxes (XML, JSON, RDF, CSV, YAML, protocol buffers, Avro) - data shapes and models (tables, trees) - type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +) - an overview of functional, declarative programming languages across data shapes (SQL, JSONiq) - the most important query paradigms (selection, projection, joining, grouping, ordering, windowing) - paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark) - resource management (YARN) - what a data center is made of and why it matters (racks, nodes, ...) - underlying architectures (internal machinery of HDFS, HBase, Spark) - optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing) - applications. Large scale analytics and machine learning are outside of the scope of this course.
Literature	Papers from scientific conferences and journals. References will be given as part of the course material during the semester.
Prerequisites / Notice	This course is not intended for Computer Science and Data Science students. Computer Science and Data Science students interested in Big Data MUST attend the Master's level Big Data lecture, offered in Fall. Requirements: programming knowledge (Java, C++, Python, PHP, ...) as well as basic knowledge on databases (SQL). If you have already built your own website with a backend SQL database, this is perfect. Attendance is especially recommended to those who attended Information Systems for Engineers last Fall, which introduced the "good old databases of the 1970s" (SQL, tables and cubes). However, this is not a strict requirement, and it is also possible to take the lectures in reverse order.
252-0312-00L	Ubiquitous Computing	W	6 credits	2V + 3A	C. Holz
Abstract	Ubiquitous Computing means interacting with information and with each other anywhere, mediated through miniature technology everywhere. We will investigate the technical aspects of Ubicomp, particularly sensing, processing, and sense making: input (touch & gesture), activity, monitoring cardiovascular health and neurological conditions, context & location sensing, affective computing.
Objective	The course will combine high-level concepts with low-level technical methods needed to sense, detect, and understand them. High-level: – input modalities for interactive systems (touch, gesture) – "activities" and "events" (exercises and other mechanical activities such as movements and resulting vibrations) – health monitoring (basic cardiovascular physiology) – location (GPS, urban simulations, smart cities and development) – affective computing (emotions, mood, personality) Low-level: – sampling (Shannon Nyquist) and filtering (FIR, IIR), time and frequency domains (Fourier transforms) – cross-modal sensor systems, signal synchronization and correlation – event detection, classification, prediction using basic signal processing as well as learning-based methods – sensor types: optical, mechanical/acoustic, electromagnetic – signals modalities and processing of: application (modalities/methods) * touch detection (resistive sensing, capacitive sensing, diffuse illumination/DI, spectral reflections, frustrated total internal reflection/FTIR, fingerprint scanning, surface-acoustic waves) * gesture recognition (inertial sensing through accelerometers, gyroscopes) * activity detection and tracking (inertial, acoustic, vibrotactile for classification, counting, vibrometry) * occupation and use (electricity monitoring, water consumption, single-point sensing) * cardiovascular (electrocardioagraphy, photoplethysmography, pulse oximetry, ballistocardiography, blood pressure, pulse transit time, bio impedance) * affective computing (heart rate variability, R-R intervals, electrodermal activity, sympathetic tone, facial expressions) * neurological (fatigue, fatigability) * location (GPS, BLE, Wifi)
Content	"The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it" — Mark Weiser, 1991. This is the premise of Ubiquitous Computing, a vision that is slowly becoming reality as everything is a device and we can interact with information and with each other anywhere, mediated through miniature technology. Along with this change, interaction modalities have changed, too, from explicit input on keyboards and mice to implicit and passively observed input through sensors in the environment (e.g., speakers, cameras, temperature/occupancy detectors) and those we now wear on our bodies (e.g., health sensors, activity sensors, miniature computers we call smartwatches). In this course, we will look at the technical side of Ubicomp, particularly – sensing (incl. 'signals', sampling, data acquisition methods, controlled user studies, uncontrolled studies in-the-wild), – processing (incl. frequencies, feature extraction, detection), and – sense making: input sensing (touch & gesture), activity sensing (motion), monitoring cardiovascular health, affective state, neurological conditions (with basics on cardiovascular physiology + PPG, PulseOx, ECG, EDA, BCG, SCG, HRV, BioZ, IPG, PAT, PTT), context & location sensing (GPS/Wifi, motion). Lectures will be accompanied by practical sessions that focus on sensor modalities and signal processing. Here, we will work on existing data sets and devise methods to record our own data for processing and prediction purposes. A series of reading assignments, covering both well-established publications in Ubicomp as well as emerging results and methods, will bridge the fundamentals and topics taught in class to academic research and real-world problems. More information on the course site: https://teaching.siplab.org/ubiquitous_computing/2021/
Lecture notes	Copies of slides will be made available. Lectures will be recorded and made available online. More information on the course site: https://teaching.siplab.org/ubiquitous_computing/2021/
Literature	Will be provided in the lecture. To put you in the mood: Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104
227-1032-00L	Neuromorphic Engineering II Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module INI405 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link	W	6 credits	5G	T. Delbrück, G. Indiveri, S.‑C. Liu
Abstract	This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I".
Objective	Design of a neuromorphic circuit for implementation with CMOS technology.
Content	This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I". The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester.
Literature	S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Prerequisites / Notice	Prerequisites: Neuromorphic Engineering I strongly recommended
227-1034-00L	Computational Vision (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: INI402 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html	W	6 credits	2V + 1U	D. Kiper
Abstract	This course focuses on neural computations that underlie visual perception. We study how visual signals are processed in the retina, LGN and visual cortex. We study the morpholgy and functional architecture of cortical circuits responsible for pattern, motion, color, and three-dimensional vision.
Objective	This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
Content	This course considers the operation of circuits in the process of neural computations. The evolution of neural systems will be considered to demonstrate how neural structures and mechanisms are optimised for energy capture, transduction, transmission and representation of information. Canonical brain circuits will be described as models for the analysis of sensory information. The concept of receptive fields will be introduced and their role in coding spatial and temporal information will be considered. The constraints of the bandwidth of neural channels and the mechanisms of normalization by neural circuits will be discussed. The visual system will form the basis of case studies in the computation of form, depth, and motion. The role of multiple channels and collective computations for object recognition will be considered. Coordinate transformations of space and time by cortical and subcortical mechanisms will be analysed. The means by which sensory and motor systems are integrated to allow for adaptive behaviour will be considered.
Literature	Books: (recommended references, not required) 1. An Introduction to Natural Computation, D. Ballard (Bradford Books, MIT Press) 1997. 2. The Handbook of Brain Theorie and Neural Networks, M. Arbib (editor), (MIT Press) 1995.
227-1046-00L	Computer Simulations of Sensory Systems	W	3 credits	3G	T. Haslwanter
Abstract	This course deals with computer simulations of the human auditory, visual, and balance system. The lecture will cover the physiological and mechanical mechanisms of these sensory systems. And in the exercises, the simulations will be implemented with Python. The simulations will be such that their output could be used as input for actual neuro-sensory prostheses.
Objective	Our sensory systems provide us with information about what is happening in the world surrounding us. Thereby they transform incoming mechanical, electromagnetic, and chemical signals into “action potentials”, the language of the central nervous system. The main goal of this lecture is to describe how our sensors achieve these transformations, how they can be reproduced with computational tools. For example, our auditory system performs approximately a “Fourier transformation” of the incoming sound waves; our early visual system is optimized for finding edges in images that are projected onto our retina; and our balance system can be well described with a “control system” that transforms linear and rotational movements into nerve impulses. In the exercises that go with this lecture, we will use Python to reproduce the transformations achieved by our sensory systems. The goal is to write programs whose output could be used as input for actual neurosensory prostheses: such prostheses have become commonplace for the auditory system, and are under development for the visual and the balance system. For the corresponding exercises, at least some basic programing experience is required!!
Content	The following topics will be covered: • Introduction into the signal processing in nerve cells. • Introduction into Python. • Simplified simulation of nerve cells (Hodgkins-Huxley model). • Description of the auditory system, including the application of Fourier transforms on recorded sounds. • Description of the visual system, including the retina and the information processing in the visual cortex. The corresponding exercises will provide an introduction to digital image processing. • Description of the mechanics of our balance system, and the “Control System”-language that can be used for an efficient description of the corresponding signal processing (essentially Laplace transforms and control systems).
Lecture notes	For each module additional material will be provided on the e-learning platform "moodle". The main content of the lecture is also available as a wikibook, under http://en.wikibooks.org/wiki/Sensory_Systems
Literature	Open source information is available as wikibook http://en.wikibooks.org/wiki/Sensory_Systems For good overviews of the neuroscience, I recommend: • Principles of Neural Science (5th Ed, 2012), by Eric Kandel, James Schwartz, Thomas Jessell, Steven Siegelbaum, A.J. Hudspeth ISBN 0071390111 / 9780071390118 THE standard textbook on neuroscience. NOTE: The 6th edition will be released on February 5, 2021! • L. R. Squire, D. Berg, F. E. Bloom, Lac S. du, A. Ghosh, and N. C. Spitzer. Fundamental Neuroscience, Academic Press - Elsevier, 2012 [ISBN: 9780123858702]. This book covers the biological components, from the functioning of an individual ion channels through the various senses, all the way to consciousness. And while it does not cover the computational aspects, it nevertheless provides an excellent overview of the underlying neural processes of sensory systems. • G. Mather. Foundations of Sensation and Perception, 2nd Ed Psychology Press, 2009 [ISBN: 978-1-84169-698-0 (hardcover), oder 978-1-84169-699-7 (paperback)] A coherent, up-to-date introduction to the basic facts and theories concerning human sensory perception. • The best place to get started with Python programming are the https://scipy-lectures.org/ On signal processing with Python, my upcoming book • Hands-on Signal Analysis with Python (Due: January 13, 2021 ISBN 978-3-030-57902-9, https://www.springer.com/gp/book/9783030579029) will contain an explanation to all the required programming tools and packages.
Prerequisites / Notice	• Since I have to gravel from Linz, Austria, to Zurich to give this lecture, I plan to hold this lecture in blocks (every 2nd week). • In addition to the lectures, this course includes external lab visits to institutes actively involved in research on the relevant sensory systems.
402-0738-00L	Statistical Methods and Analysis Techniques in Experimental Physics	W	10 credits	5G	M. Donegà
Abstract	This lecture gives an introduction to the statistical methods and the various analysis techniques applied in experimental particle physics. The exercises treat problems of general statistical topics; they also include hands-on analysis projects, where students perform independent analyses on their computer, based on real data from actual particle physics experiments.
Objective	Students will learn the most important statistical methods used in experimental particle physics. They will acquire the necessary skills to analyse large data records in a statistically correct manner. Learning how to present scientific results in a professional manner and how to discuss them.
Content	Topics include: - modern methods of statistical data analysis - probability distributions, error analysis, simulation methos, hypothesis testing, confidence intervals, setting limits and introduction to multivariate methods. - most examples are taken from particle physics. Methodology: - lectures about the statistical topics; - common discussions of examples; - exercises: specific exercises to practise the topics of the lectures; - all students perform statistical calculations on (their) computers; - students complete a full data analysis in teams (of two) over the second half of the course, using real data taken from particle physics experiments; - at the end of the course, the students present their analysis results in a scientific presentation; - all students are directly tutored by assistants in the classroom.
Lecture notes	- Copies of all lectures are available on the web-site of the course. - A scriptum of the lectures is also available to all students of the course.
Literature	1) Statistics: A guide to the use of statistical medhods in the Physical Sciences, R.J.Barlow; Wiley Verlag . 2) J Statistical data analysis, G. Cowan, Oxford University Press; ISBN: 0198501552. 3) Statistische und numerische Methoden der Datenanalyse, V.Blobel und E.Lohrmann, Teubner Studienbuecher Verlag. 4) Data Analysis, a Bayesian Tutorial, D.S.Sivia with J.Skilling, Oxford Science Publications.
Prerequisites / Notice	Basic knowlege of nuclear and particle physics are prerequisites.
636-0016-00L	Computational Systems Biology: Stochastic Approaches	W	4 credits	3G	M. H. Khammash, A. Gupta
Abstract	This course is concerned with the development of computational methods for modeling, simulation, and analysis of stochasticity in living cells. Using these tools, the course explores the richness of stochastic phenomena, how it arises from the interactions of dynamics and noise, and its biological implications.
Objective	To understand the origins and implications of stochastic noise in living cells, and to learn the computational tools for the modeling, simulation, analysis, and identification of stochastic biochemical reaction networks.
Content	The cellular environment is abuzz with noise. A key source of this noise is the randomness that characterizes the motion of cellular constituents at the molecular level. Cellular noise not only results in random fluctuations (over time) within individual cells, but it is also a main source of phenotypic variability among clonal cell populations. Review of basic probability and stochastic processes; Introduction to stochastic gene expression; deterministic vs. stochastic models; the stochastic chemical kinetics framework; a rigorous derivation of the chemical master equation; moment computations; linear vs. nonlinear propensities; linear noise approximations; Monte Carlo simulations; Gillespie's Stochastic Simulation Algorithm (SSA) and variants; direct methods for the solution of the Chemical Master Equation; moment closure methods; intrinsic and extrinsic noise in gene expression; parameter identification from noise; propagation of noise in cell networks; noise suppression in cells; the role of feedback; exploiting noise; bimodality and stochastic switches.
Literature	Literature will be distributed during the course as needed.
Prerequisites / Notice	Students are expected to have completed the course `Mathematical modeling for systems biology (BSc Biotechnology) or `Computational systems biology (MSc Computational biology and bioinformatics). Concurrent enrollment in `Computational Systems Biology: Deterministic Approaches is recommended.
701-0412-00L	Climate Systems	W	3 credits	2G	S. I. Seneviratne, L. Gudmundsson
Abstract	This course introduces the most important physical components of the climate system and their interactions. The mechanisms of anthropogenic climate change are analysed against the background of climate history and variability. Those completing the course will be in a position to identify and explain simple problems in the area of climate systems.
Objective	Students are able - to describe the most important physical components of the global climate system and sketch their interactions - to explain the mechanisms of anthropogenic climate change - to identify and explain simple problems in the area of climate systems
Lecture notes	Copies of the slides are provided in electronic form.
Literature	A comprehensive list of references is provided in the class. Two books are particularly recommended: - Hartmann, D., 2016: Global Physical Climatology. Academic Press, London, 485 pp. - Peixoto, J.P. and A.H. Oort, 1992: Physics of Climate. American Institute of Physics, New York, 520 pp.
Prerequisites / Notice	Teaching: Sonia I. Seneviratne & Lukas Gudmundsson, several keynotes to special topics by other professors Course taught in german/english, slides in english
327-2201-00L	Transport Phenomena II	W	5 credits	4G	J. Vermant
Abstract	Numerical and analytical methods for real-world "Transport Phenomena"; atomistic understanding of transport properties based on kinetic theory and mesoscopic models; fundamentals, applications, and simulations
Objective	The teaching goals of this course are on five different levels: (1) Deep understanding of fundamentals: kinetic theory, mesoscopic models, ... (2) Ability to use the fundamental concepts in applications (3) Insight into the role of boundary conditions (4) Knowledge of a number of applications (5) Flavor of numerical techniques: finite elements, lattice Boltzmann, ...
Content	Thermodynamics of Interfaces Interfacial Balance Equations Interfacial Force-Flux Relations Polymer Processing Transport Around a Sphere Refreshing Topics in Equilibrium Statistical Mechanics Kinetic Theory of Gases Kinetic Theory of Polymeric Liquids Transport in Biological Systems Dynamic Light Scattering
Lecture notes	The course is based on the book D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018)
Literature	1. D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018) 2. R. B. Bird, W. E. Stewart, and E. N. Lightfoot, Transport Phenomena, 2nd Ed. (Wiley, 2001) 3. Deen,W. Analysis of Transport Phenomena, Oxford University Press, 2012 4. R. B. Bird, Five Decades of Transport Phenomena (Review Article), AIChE J. 50 (2004) 273-287
Prerequisites / Notice	Complex numbers. Vector analysis (integrability; Gauss' divergence theorem). Laplace and Fourier transforms. Ordinary differential equations (basic ideas). Linear algebra (matrices; functions of matrices; eigenvectors and eigenvalues; eigenfunctions). Probability theory (Gaussian distributions; Poisson distributions; averages; moments; variances; random variables). Numerical mathematics (integration). Statistical thermodynamics (Gibbs' fundamental equation; thermodynamic potentials; Legendre transforms; Gibbs' phase rule; ergodicity; partition functions; Einstein's fluctuation theory). Linear irreversible thermodynamics (forces and fluxes; Fourier's, Newton's and Fick's laws for fluxes). Hydrodynamics (local equilibrium; balance equations for mass, momentum, energy and entropy). Programming and simulation techniques (Matlab, Monte Carlo simulations).
401-3902-21L	Network & Integer Optimization: From Theory to Application	W	6 credits	3G	R. Zenklusen
Abstract	This course covers various topics in Network and (Mixed-)Integer Optimization. It starts with a rigorous study of algorithmic techniques for some network optimization problems (with a focus on matching problems) and moves to key aspects of how to attack various optimization settings through well-designed (Mixed-)Integer Programming formulations.
Objective	Our goal is for students to both get a good foundational understanding of some key network algorithms and also to learn how to effectively employ (Mixed-)Integer Programming formulations, techniques, and solvers, to tackle a wide range of discrete optimization problems.
Content	Key topics include: - Matching problems; - Integer Programming techniques and models; - Extended formulations and strong problem formulations; - Solver techniques for (Mixed-)Integer Programs; - Decomposition approaches.
Literature	- Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018. - Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes. - Vanderbeck François, Wolsey Laurence: Reformulations and Decomposition of Integer Programs. Chapter 13 in: 50 Years of Integer Programming 1958-2008. Springer, 2010. - Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986.
Prerequisites / Notice	Solid background in linear algebra. Preliminary knowledge of Linear Programming is ideal but not a strict requirement. Prior attendance of the course Mathematical Optimization is a plus.
401-3908-21L	Polynomial Optimization	W	6 credits	3G	A. A. Kurpisz
Abstract	Introduction to Polynomial Optimization and methods to solve its convex relaxations.
Objective	The goal of this course is to provide a treatment of non-convex Polynomial Optimization problems through the lens of various techniques to solve its convex relaxations. Part of the course will be focused on learning how to apply these techniques to practical examples in finance, robotics and control.
Content	Key topics include: - Polynomial Optimization as a non-convex optimization problem and its connection to certifying non-negativity of polynomials - Optimization-free and Linear Programming based techniques to approach Polynomial Optimization problems. - Introduction of Second-Order Cone Programming, Semidefinite Programming and Relative Entropy Programming as a tool to solve relaxations of Polynomial Optimization problems. - Applications to optimization problems in finance, robotics and control.
Lecture notes	A script will be provided.
Literature	Other helpful materials include: - Jean Bernard Lasserre, An Introduction to Polynomial and Semi-Algebraic Optimization, Cambridge University Press, February 2015 - Pablo Parrilo. 6.972 Algebraic Techniques and Semidefinite Optimization. Spring 2006. Massachusetts Institute of Technology: MIT OpenCourseWare, . License: .
Prerequisites / Notice	Background in Linear and Integer Programming is recommended.
351-1138-00L	PRISMA Capstone - Rethinking Sustainable Cities and Communities Bachelor students get preferential access to this course. All interested students must apply through a separate application process at: https://mtecethz.qualtrics.com/jfe/form/SV_cx4ZghhYhQAY3nT Participation is subject to successful selection through this sign-up process.	W	4 credits	4V	A. Cabello Llamas, M. Augsburger
Abstract	The goal of this intense one-week course is to bring students from different backgrounds together to make connections between disciplines and to build bridges to society. Supported by student coaches and experts, our student teams will use hands-on Design Thinking methods to address relevant challenges based on the UN sustainable development goals.
Objective	In this intense 7-day block course students will be able to acquire and practice essential cross-disciplinary competencies as well as gaining an understanding of a human-centered innovation process. More specifically students will learn to: - Work and think in a problem-based way. - Put their own field into a broader context. - Engage in collaborative ideation with a multidisciplinary team. - Identify challenges related to relevant societal issues. - Develop, prototype and plan innovative solutions for a range of different contexts. - Innovate in a human-centered way by observing and interacting with key stakeholders. The acquired methods and skills are based on the ETH competence framework and can be applied to tackle a broad range of problems in academia and society. Moving beyond traditional teaching approaches, this course allows students to engage creatively in a process of rethinking and redesigning aspects and elements of current and future urban areas, actively contributing towards fulfilling the UN SDG 11.
Content	The course is divided in to three stages: Warm-up and framing: The goal of this first stage is to get familiar with current problems faced by cities and communities as well as with the Design Thinking process and mindset. The students will learn about the working process, the teaching spaces and resources, as well as their fellow students and the lecturers. Identifying challenges: The objective is to get to know additional methods and tools to identify a specific challenge relevant for urban areas through fieldwork and direct engagement with relevant stakeholders, resulting in the definition of an actionable problem statement that will form the starting point for the development of innovative solutions. Solving challenges within current and future context: During this phase, students will apply the learned methods and tools to solve the identified challenge in a multi-disciplinary group by creating, developing and testing high-potential ideas. The ideas are presented to relevant academic, industry and societal stakeholders on the last day of the week. To facilitate the fast-paced innovation journey, the multidisciplinary teams are supported throughout the week by experienced student coaches. This course is a capstone for the student-lead initiative PRISMA. (https://www.prisma.ethz.ch/).
Prerequisites / Notice	Bachelor students get preferential access to this course. All interested students must apply through a separate application process at: https://mtecethz.qualtrics.com/jfe/form/SV_cx4ZghhYhQAY3nT Participation is subject to successful selection through this sign-up process.
» see also Fields of Specialization
» Electives (CSE Master)

Additional Electives from the Fields of Specialization (CSE Master) recognition of 227-0662-00L and 227-0662-10L requires the successful completion of both course units
Number	Title	Type	ECTS	Hours	Lecturers
701-1228-00L	Cloud Dynamics: Hurricanes	W	4 credits	3G	U. Lohmann
Abstract	Hurricanes are among the most destructive elements in the atmosphere. This lecture will discuss the physical requirements for their formation, life cycle, damage potential and their relationship to global warming. It also distinguishes hurricanes from thunderstorms and tornadoes.
Objective	At the end of this course students will be able to distinguish the formation and life cycle mechanisms of tropical cyclones from those of extratropical thunderstorms/cyclones, project how tropical cyclones change in a warmer climate based on their physics and evaluate different tropical cyclone modification ideas.
Content	see course outline at: https://iac.ethz.ch/edu/courses/master/modules/cloud-dynamics
Lecture notes	Slides will be made available
Literature	A literature list can be found here: https://www.iac.ethz.ch/edu/courses/master/modules/cloud_dynamics
Prerequisites / Notice	At least one introductory lecture in Atmospheric Science or Instructor's consent. This lecture will build on some concepts of atmospheric dynamics and their governing equations. Thus, mathematical knowledge will be needed to use the equations to understand the material of the course.
701-1270-00L	High Performance Computing for Weather and Climate	W	3 credits	3G	O. Fuhrer
Abstract	State-of-the-art weather and climate simulations rely on large and complex software running on supercomputers. This course focuses on programming methods and tools for understanding, developing and optimizing the computational aspects of weather and climate models. Emphasis will be placed on the foundations of parallel computing, practical exercises and emerging trends such as using GPUs.
Objective	After attending this course, students will be able to: - Understand a broad variety of high performance computing concepts relevant for weather and climate simulations - Work with weather and climate simulation codes that run on large supercomputers
Content	HPC Overview: - Why does weather and climate require HPC? - Today's HPC: Beowulf-style clusters, massively parallel architectures, hybrid computing, accelerators - Scaling / Parallel efficiency - Algorithmic motifs in weather and climate Writing HPC code: - Data locality and single node efficiency - Shared memory parallelism with OpenMP - Distributed memory parallelism with MPI - GPU computing - High-level programming and domain-specific languages
Literature	- Introduction to High Performance Computing for Scientists and Engineers, G. Hager and G. Wellein, CRC Press, 2011 - Computer Organization and Design, D.H. Patterson and J.L. Hennessy - Parallel Computing, A. Grama, A. Gupta, G. Karypis, V. Kumar (https://www-users.cs.umn.edu/~karypis/parbook/) - Parallel Programming in MPI and OpenMP, V. Eijkhout (http://pages.tacc.utexas.edu/~eijkhout/pcse/html/index.html)
Prerequisites / Notice	- fundamentals of numerical analysis and atmospheric modeling - basic experience in a programming language (C/C++, Fortran, Python, …) - experience using command line interfaces in *nix environments (e.g., Unix, Linux)
151-0110-00L	Compressible Flows	W	4 credits	2V + 1U	T. Rösgen
Abstract	Topics: unsteady one-dimensional subsonic and supersonic flows, acoustics, sound propagation, supersonic flows with shocks and Prandtl-Meyer expansions, flow around slender bodies, shock tubes, reaction fronts (deflagration and detonation). Mathematical tools: method of characteristics and selected numerical methods.
Objective	Illustration of compressible flow phenomena and introduction to the corresponding mathematical description methods.
Content	The interaction of compressibility and inertia is responsible for wave generation in a fluid. The compressibility plays an important role for example in unsteady phenomena, such as oscillations in gas pipelines or exhaust pipes. Compressibility effects are also important in steady subsonic flows with high Mach numbers (M>0.3) and in supersonic flows (e.g. aeronautics, turbomachinery). The first part of the lecture deals with wave propagation phenomena in one-dimensional subsonic and supersonic flows. The discussion includes waves with small amplitudes in an acoustic approximation and waves with large amplitudes with possible shock formation. The second part deals with plane, steady supersonic flows. Slender bodies in a parallel flow are considered as small perturbations of the flow and can be treated by means of acoustic methods. The description of the two-dimensional supersonic flow around bodies with arbitrary shapes includes oblique shocks and Prandtl-Meyer expansions etc.. Various boundary conditions, which are imposed for example by walls or free-jet boundaries, and interactions, reflections etc. are taken into account.
Lecture notes	not available
Literature	a list of recommended textbooks is handed out at the beginning of the lecture.
Prerequisites / Notice	prerequisites: Fluiddynamics I and II
327-0613-00L	Computer Applications: Finite Elements in Solids and Structures The course will only take place if at least 7 students are enrolled.	W	4 credits	2V + 2U	A. Gusev
Abstract	To introduce the Finite Element Method to the students with a general interest in the topic
Objective	To introduce the Finite Element Method to the students with a general interest in the topic
Content	Introduction; Energy formulations; Displacement finite elements; Solutions to the finite element equations; Linear elements; Convergence, compatibility and completeness; Higher order elements; Beam and frame elements, Plate and shell elements; Dynamics and vibration; Generalization of the Finite Element concepts (Galerkin-weighted residual and variational approaches)
Lecture notes	Autographie
Literature	- Astley R.J. Finite Elements in Solids and Structures, Chapman & Hill, 1992 - Zienkiewicz O.C., Taylor R.L. The Finite Element Method, 5th ed., vol. 1, Butterworth-Heinemann, 2000
151-0212-00L	Advanced CFD Methods	W	4 credits	2V + 1U	P. Jenny
Abstract	Fundamental and advanced numerical methods used in commercial and open-source CFD codes will be explained. The main focus is on numerical methods for conservation laws with discontinuities, which is relevant for trans- and hypersonic gas dynamics problems, but also CFD of incompressible flows, Direct Simulation Monte Carlo and the Lattice Boltzmann method are explained.
Objective	Knowing what's behind a state-of-the-art CFD code is not only important for developers, but also for users in order to choose the right methods and to achieve meaningful and accurate numerical results. Acquiring this knowledge is the main goal of this course. Established numerical methods to solve the incompressible and compressible Navier-Stokes equations are explained, whereas the focus lies on finite volume methods for compressible flow simulations. In that context, first the main theory and then numerical schemes related to hyperbolic conservation laws are explained, whereas not only examples from fluid mechanics, but also simpler, yet illustrative ones are considered (e.g. Burgers and traffic flow equations). In addition, two less commonly used yet powerful approaches, i.e., the Direct Simulation Monte Carlo (DSMC) and Lattice Boltzmann methods, are introduced. For most exercises a C++ code will have to be modified and applied.
Content	- Finite-difference vs. finite-element vs. finite-volume methods - Basic approach to simulate incompressible flows - Brief introduction to turbulence modeling - Theory and numerical methods for compressible flow simulations - Direct Simulation Monte Carlo (DSMC) - Lattice Boltzmann method
Lecture notes	Part of the course is based on the referenced books. In addition, the participants receive a manuscript and the slides.
Literature	"Computational Fluid Dynamics" by H. K. Versteeg and W. Malalasekera. "Finite Volume Methods for Hyperbolic Problems" by R. J. Leveque.
Prerequisites / Notice	Basic knowledge in - fluid dynamics - numerical mathematics - programming (programming language is not important, but C++ is of advantage)
401-8908-00L	Continuous Time Quantitative Finance (University of Zurich) No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH. UZH Module Code: MFOEC204 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html	W	3 credits	3V	University lecturers
Abstract	American Options, Stochastic Volatility, Lévy Processes and Option Pricing, Exotic Options, Transaction Costs and Real Options.
Objective	The course focuses on the theoretical foundations of modern derivative pricing. It aims at deriving and explaining important option pricing models by relying on some mathematical tools of continuous time finance. A particular focus on jump processes is given. The introduction of possible financial crashes is now essential in some models and a clear understanding of Poisson processes is therefore important. A standard background in stochastic calculus is required.
Content	Stochastic volatility models Itô's formula and Girsanov theorem for jump-diffusion processes The pricing of options in presence of possible discontinuities Exotic options Transaction costs
Lecture notes	See: http://www.isb.uzh.ch/institut/staff/chesney.marc/teaching/
Literature	See: http://www.isb.uzh.ch/institut/staff/chesney.marc/teaching/
Prerequisites / Notice	This course replaces "Continuous Time Quantitative Finance" (MFOEC108), which will be discontinued. Students who have taken "Continuous Time Quantitative Finance" (MFOEC108) in the past, are not allowed to book this course "Continuous Time Quantitative Finance" (MFOEC204).
227-0662-00L	Organic and Nanostructured Optics and Electronics (Course) Does not take place this semester.	W	3 credits	2G	V. Wood
Abstract	This course examines the optical and electronic properties of excitonic materials that can be leveraged to create thin-film light emitting devices and solar cells. Laboratory sessions provide students with experience in synthesis and optical characterization of nanomaterials as well as fabrication and characterization of thin film devices.
Objective	Gain the knowledge and practical experience to begin research with organic or nanostructured materials and understand the key challenges in this rapidly emerging field.
Content	0-Dimensional Excitonic Materials (organic molecules and colloidal quantum dots) Energy Levels and Excited States (singlet and triplet states, optical absorption and luminescence). Excitonic and Polaronic Processes (charge transport, Dexter and Förster energy transfer, and exciton diffusion). Devices (photodetectors, solar cells, and light emitting devices).
Literature	Lecture notes and reading assignments from current literature to be posted on website.
227-0662-10L	Organic and Nanostructured Optics and Electronics (Project) Does not take place this semester.	W	3 credits	2A	V. Wood
Abstract	This course examines the optical and electronic properties of excitonic materials that can be leveraged to create thin-film light emitting devices and solar cells. Laboratory sessions provide students with experience in synthesis and optical characterization of nanomaterials as well as fabrication and characterization of thin film devices.
Objective	Gain the knowledge and practical experience to begin research with organic or nanostructured materials and understand the key challenges in this rapidly emerging field.
Content	0-Dimensional Excitonic Materials (organic molecules and colloidal quantum dots) Energy Levels and Excited States (singlet and triplet states, optical absorption and luminescence). Excitonic and Polaronic Processes (charge transport, Dexter and Förster energy transfer, and exciton diffusion). Devices (photodetectors, solar cells, and light emitting devices).
Literature	Lecture notes and reading assignments from current literature to be posted on website.
Prerequisites / Notice	Admission is conditional to passing 227-0662-00L Organic and Nanostructured Optics and Electronics (Course)
262-0200-00L	Bayesian Phylodynamics – Taming the BEAST	W	4 credits	2G + 2A	T. Stadler, T. Vaughan
Abstract	How fast is COVID-19 spreading at the moment? How fast was Ebola spreading in West Africa? Where and when did these epidemic outbreak start? How can we construct the phylogenetic tree of great apes, and did gene flow occur between different apes? At the end of the course, students will have designed, performed, presented, and discussed their own phylodynamic data analysis to answer such questions.
Objective	Attendees will extend their knowledge of Bayesian phylodynamics obtained in the “Computational Biology” class (636-0017-00L) and will learn how to apply this theory to real world data. The main theoretical concepts introduced are: * Bayesian statistics * Phylogenetic and phylodynamic models * Markov Chain Monte Carlo methods Attendees will apply these concepts to a number of applications yielding biological insight into: * Epidemiology * Pathogen evolution * Macroevolution of species
Content	During the first part of the block course, the theoretical concepts of Bayesian phylodynamics will be presented by us as well as leading international researchers in that area. The presentations will be followed by attendees using the software package BEAST v2 to apply these theoretical concepts to empirical data. We will use previously published datasets on e.g. COVID-19, Ebola, Zika, Yellow Fever, Apes, and Penguins for analysis. Examples of these practical tutorials are available on https://taming-the-beast.org/. In the second part of the block course, students choose an empirical dataset of genetic sequencing data and possibly some non-genetic metadata. They then design and conduct a research project in which they perform Bayesian phylogenetic analyses of their dataset. A final written report on the research project has to be submitted after the block course for grading.
Lecture notes	All material will be available on https://taming-the-beast.org/.
Literature	The following books provide excellent background material: • Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST. • Yang, Z. 2014. Molecular Evolution: A Statistical Approach. • Felsenstein, J. 2003. Inferring Phylogenies. More detailed information is available on https://taming-the-beast.org/.
Prerequisites / Notice	This class builds upon the content which we teach in the Computational Biology class (636-0017-00L). Attendees must have either taken the Computational Biology class or acquired the content elsewhere.
701-1708-00L	Infectious Disease Dynamics	W	4 credits	2V	S. Bonhoeffer, R. D. Kouyos, R. R. Regös, T. Stadler
Abstract	This course introduces into current research on the population biology of infectious diseases. The course discusses the most important mathematical tools and their application to relevant diseases of human, natural or managed populations.
Objective	Attendees will learn about: * the impact of important infectious pathogens and their evolution on human, natural and managed populations * the population biological impact of interventions such as treatment or vaccination * the impact of population structure on disease transmission Attendees will learn how: * the emergence spread of infectious diseases is described mathematically * the impact of interventions can be predicted and optimized with mathematical models * population biological models are parameterized from empirical data * genetic information can be used to infer the population biology of the infectious disease The course will focus on how the formal methods ("how") can be used to derive biological insights about the host-pathogen system ("about").
Content	After an introduction into the history of infectious diseases and epidemiology the course will discuss basic epidemiological models and the mathematical methods of their analysis. We will then discuss the population dynamical effects of intervention strategies such as vaccination and treatment. In the second part of the course we will introduce into more advanced topics such as the effect of spatial population structure, explicit contact structure, host heterogeneity, and stochasticity. In the final part of the course we will introduce basic concepts of phylogenetic analysis in the context of infectious diseases.
Lecture notes	Slides and script of the lecture will be available online.
Literature	The course is not based on any of the textbooks below, but they are excellent choices as accompanying material: * Keeling & Rohani, Modeling Infectious Diseases in Humans and Animals, Princeton Univ Press 2008 * Anderson & May, Infectious Diseases in Humans, Oxford Univ Press 1990 * Murray, Mathematical Biology, Springer 2002/3 * Nowak & May, Virus Dynamics, Oxford Univ Press 2000 * Holmes, The Evolution and Emergence of RNA Viruses, Oxford Univ Press 2009
Prerequisites / Notice	Basic knowledge of population dynamics and population genetics as well as linear algebra and analysis will be an advantage.

Case Studies
Number	Title	Type	ECTS	Hours	Lecturers
401-3667-21L	Case Studies Seminar (Spring Semester 2021)	W	3 credits	2S	V. C. Gradinaru, R. Hiptmair, R. Käppeli, M. Reiher
Abstract	In the CSE Case Studies Seminar invited speakers from ETH, from other universities as well as from industry give a talk on an applied topic. Beside of attending the scientific talks students are asked to give short presentations (10 minutes) on a published paper out of a list.
Objective
Content	In the CSE Case Studies Seminar invited speakers from ETH, from other universities as well as from industry give a talk on an applied topic. Beside of attending the scientific talks students are asked to give short presentations (10 minutes) on a published paper out of a list (containing articles from, e.g., Nature, Science, Scientific American, etc.). If the underlying paper comprises more than 15 pages, two or three consecutive case studies presentations delivered by different students can be based on it. Consistency in layout, style, and contents of those presentations is expected.
Prerequisites / Notice	In Spring 2020 the talks will be given via Zoom. About the video conferencing system Zoom: Zoom is a do-it-yourself video conferencing system supported by ETH. With Zoom, one person can give a lecture with a presentation and up to 100 people can join in via chat or audio connection.Use the provided link to enter the Zoom room at the designated time. Download/Open the Zoom App or join the meeting via the browser. Please test whether you can join the room and whether the audio works properly beforehand. We recommend you use a headset in order to minimize unwanted sounds from your environment. More Info: https://support.zoom.us/hc/en-us/articles/201362193-Joining-a-Meeting Link https://support.zoom.us/hc/en-us

GESS Science in Perspective
Science in Perspective
» see Science in Perspective: Type A: Enhancement of Reflection Capability
» Recommended Science in Perspective (Type B) for D-MATH

Language Courses
» siehe Studiengang Wissenschaft im Kontext: Sprachkurse ETH/UZH

Colloquia
Number	Title	Type	ECTS	Hours	Lecturers
401-5650-00L	Zurich Colloquium in Applied and Computational Mathematics	E-	0 credits	1K	R. Abgrall, R. Alaifari, H. Ammari, R. Hiptmair, S. Mishra, S. Sauter, C. Schwab
Abstract	Research colloquium
Objective

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