Search result: Catalogue data in Autumn Semester 2022

Computational Biology and Bioinformatics Master Information
More information at: Link
Core Courses
Please note that the list of core courses is a closed list. Other courses cannot be added to the core course category in the study plan. Also the assignments of courses to core subcategories cannot be changed.
Students need to pass at least one course in each core subcategory.
A total of 40 ECTS needs to be acquired in the core course category.
Bioinformatics
Please note that all Bioinformatics core courses are offered in the autumn semester
NumberTitleTypeECTSHoursLecturers
636-0009-00LEvolutionary DynamicsW6 credits2V + 1U + 2AN. Beerenwinkel
AbstractEvolutionary dynamics is concerned with the mathematical principles according to which life has evolved. This course offers an introduction to mathematical modeling of evolution, including deterministic and stochastic models, with an emphasis on tumor evolution.
ObjectiveThe goal of this course is to understand and to appreciate mathematical models and computational methods that provide insight into the evolutionary process in general and tumor evolution in particular. Students should analyze and evaluate models and their application critically and be able to design new models.
ContentEvolution is the one theory that encompasses all of biology. It provides a single, unifying concept to understand the living systems that we observe today. We will introduce several types of mathematical models of evolution to describe gene frequency changes over time in the context of different biological systems, focusing on asexual populations. Viruses and cancer cells provide the most prominent examples of such systems and they are at the same time of great biomedical interest. The course will cover some classical mathematical population genetics and population dynamics, and also introduce several new approaches. This is reflected in a diverse set of mathematical concepts which make their appearance throughout the course, all of which are introduced from scratch. Topics covered include the quasispecies equation, evolution of HIV, evolutionary game theory, evolutionary stability, evolutionary graph theory, tumor evolution, stochastic tunneling, genetic progression of cancer, diffusion theory, fitness landscapes, branching processes, and evolutionary escape.
Lecture notesNo.
Literature- Evolutionary Dynamics. Martin A. Nowak. The Belknap Press of Harvard University Press, 2006.
- Evolutionary Theory: Mathematical and Conceptual Foundations. Sean H. Rice. Sinauer Associates, Inc., 2004.
Prerequisites / NoticePrerequisites: Basic mathematics (linear algebra, calculus, probability)
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Social CompetenciesCommunicationfostered
Cooperation and Teamworkfostered
Personal CompetenciesCritical Thinkingassessed
Self-direction and Self-management fostered
636-0017-00LComputational BiologyW6 credits3G + 2AT. Vaughan, C. Magnus, T. Stadler
AbstractThe aim of the course is to provide up-to-date knowledge on how we can study biological processes using genetic sequencing data. Computational algorithms extracting biological information from genetic sequence data are discussed, and statistical tools to understand this information in detail are introduced.
ObjectiveAttendees will learn which information is contained in genetic sequencing data and how to extract information from this data using computational tools. The main concepts introduced are:
* stochastic models in molecular evolution
* phylogenetic & phylodynamic inference
* maximum likelihood and Bayesian statistics
Attendees will apply these concepts to a number of applications yielding biological insight into:
* epidemiology
* pathogen evolution
* macroevolution of species
ContentThe course consists of four parts. We first introduce modern genetic sequencing technology, and algorithms to obtain sequence alignments from the output of the sequencers. We then present methods for direct alignment analysis using approaches such as BLAST and GWAS. Second, we introduce mechanisms and concepts of molecular evolution, i.e. we discuss how genetic sequences change over time. Third, we employ evolutionary concepts to infer ancestral relationships between organisms based on their genetic sequences, i.e. we discuss methods to infer genealogies and phylogenies. Lastly, we introduce the field of phylodynamics, the aim of which is to understand and quantify population dynamic processes (such as transmission in epidemiology or speciation & extinction in macroevolution) based on a phylogeny. Throughout the class, the models and methods are illustrated on different datasets giving insight into the epidemiology and evolution of a range of infectious diseases (e.g. HIV, HCV, influenza, Ebola). Applications of the methods to the field of macroevolution provide insight into the evolution and ecology of different species clades. Students will be trained in the algorithms and their application both on paper and in silico as part of the exercises.
Lecture notesLecture slides will be available on moodle.
LiteratureThe course is not based on any of the textbooks below, but they are excellent choices as accompanying material:
* Yang, Z. 2006. Computational Molecular Evolution.
* Felsenstein, J. 2004. Inferring Phylogenies.
* Semple, C. & Steel, M. 2003. Phylogenetics.
* Drummond, A. & Bouckaert, R. 2015. Bayesian evolutionary analysis with BEAST.
Prerequisites / NoticeBasic knowledge in linear algebra, analysis, and statistics will be helpful. Programming in R will be required for the project work (compulsory continuous performance assessments). In case you do not have any previous experience with R, we strongly recommend to get familiar with R prior to the semester start. For the D-BSSE students, we highly recommend the voluntary course „Introduction to Programming“, which takes place in Basel before the start of the semester.
262-6100-00LEvolutionary GeneticsW4 credits3Gexternal organisers
AbstractEvolutionary genetics covers three important areas of modern evolutionary genetics: bioinformatics, molecular evolution and population genetics. Treating these three areas together in a single course provides an integrated education in evolutionary genetics. A solid understanding of these areas is also central to other fields such as conservation biology or behavioural and evolutionary ecology.
Objective
262-6110-00LBioinformatics AlgorithmsW4 credits3Gexternal organisers
AbstractIn this lecture, an introduction into main bioinformatics algorithms is provided. We will discuss both "classical" topics such as Hidden Markov Models, Markov chains, phylogenetic trees and "modern" approaches based on sophisticated (deep) learning models.
ObjectiveStudents can understand the main algorithmic design principles for problems like sequence alignment, motif finding and phylogenetic inference. Further, students get an overview of modern machine learning methods and their applications to bio-medical problems.
401-6282-00LStatistical Analysis of High-Throughput Genomic and Transcriptomic Data (University of Zurich)
No enrolment to this course at ETH Zurich. Book the corresponding module directly at UZH as an incoming student.
UZH Module Code: STA426

Mind the enrolment deadlines at UZH:
Link
W5 credits3GH. Rehrauer, M. Robinson
AbstractA range of topics will be covered, including basic molecular biology, genomics technologies and in particular, a wide range of statistical and computational methods that have been used in the analysis of DNA microarray and high throughput sequencing experiments.
Objective-Understand the fundamental "scientific process" in the field of Statistical Bioinformatics
-Be equipped with the skills/tools to preprocess genomic data (Unix, Bioconductor, mapping, etc.) and ensure reproducible research (Sweave)
-Have a general knowledge of the types of data and biological applications encountered with microarray and sequencing data
-Have the general knowledge of the range of statistical methods that get used with microarray and sequencing data
-Gain the ability to apply statistical methods/knowledge/software to a collaborative biological project
-Gain the ability to critical assess the statistical bioinformatics literature
-Write a coherent summary of a bioinformatics problem and its solution in statistical terms
ContentLectures will include: microarray preprocessing; normalization; exploratory data analysis techniques such as clustering, PCA and multidimensional scaling; Controlling error rates of statistical tests (FPR versus FDR versus FWER); limma (linear models for microarray analysis); mapping algorithms (for RNA/ChIP-seq); RNA-seq quantification; statistical analyses for differential count data; isoform switching; epigenomics data including DNA methylation; gene set analyses; classification
Lecture notesLecture notes, published manuscripts
Prerequisites / NoticePrerequisites: Basic knowlegde of the programming language R, sufficient knowledge in statistics

Former course title: Statistical Methods for the Analysis of Microarray and Short-Read Sequencing Data
Biophysics
NumberTitleTypeECTSHoursLecturers
262-6106-00LCurrent Topics in Biophysics Restricted registration - show details W6 credits3Gexternal organisers
AbstractThis course reviews how ideas and concepts from physics have helped understanding biological systems by discussing landmark papers in the field.
Objective
636-0104-00LBiophysical MethodsW4 credits3GD. J. Müller
AbstractStudents will be imparted knowledge in basic and advanced biophysical methods applied to problems in molecular biotechnology. The course is fundamental to applying the methods in their daily and advanced research routines. The students will learn the physical basis of the methods as well as their limitations and possibilities to address existing and future topics in molecular biotechnology.
ObjectiveGain of interdisciplinary competence in experimental and theoretical research, which qualifies for academic scientific work (master's or doctoral thesis) as well as for research in a biotechnology or a pharmaceutical company. The module is of general use in courses focused on modern biomolecular technologies, systems biology and systems engineering.
ContentThe students will learn basic and advanced knowledge in applying biophysical methods to address problems and overcome challenges in biotechnology, cell biology and life sciences in general. The biological and physical possibilities and limitations of the methods will be discussed and critically evaluated. By the end of the course the students will have assimilated knowledge on a portfolio of biophysical tools widening their research capabilities and aptitude.
The biophysical methods to be taught will include:
• Light microscopy: Resolution limit of light microscopy, fluorescence, GFP, fluorescence microscopy, DIC, phase contrast, difference between wide-field and confocal microscopy
• Super resolution optical microscopy: STED, PALM, STORM, other variations
• Electron microscopy: Scanning electron microscopy, transmission electron microscopy, electron tomography, cryo-electron microscopy, single particle analysis and averaging, tomography, sectioning, negative stain
• X-ray, electron and neutron diffraction
• MRI Imaging
• Scanning tunnelling microscopy and atomic force microscopy
• Patch clamp technologies: Principles of patch clamp analysis and application. Various patch clamp approaches used in research and industry
• Surface plasmon resonance-based biosensors
• Molecular pore-based sensors and sequencing devices
• Mechanical molecular and cellular assembly devices
• Optical and magnetic tweezers
• CD spectroscopy
• Optogenetics
• Molecular dynamics simulations
Lecture notesHand out will be given to students at lecture.
LiteratureMethods in Molecular Biophysics (5th edition), Serdyuk et al., Cambridge University Press
Biochemistry (5th edition), Berg, Tymoczko, Stryer; ISBN 0-7167-4684-0, Freeman
Bioanalytics, Lottspeich & Engels, Wiley VCH, ISBN-10: 3527339191
Cell Biology, Pollard & Earnshaw; ISBN:0-7216-3997-6, Saunder, Pennsylvania
Methods in Modern Biophysics, Nölting, 3rd Edition, Springer, ISBN-10: 3642030211
Prerequisites / NoticeThe module is composed of 3 SWS (3 hours/week): 2-hour lecture, 1-hour seminar. For the seminar, students will prepare oral presentations on specific in-depth subjects with/under the guidance of the teacher.
529-0004-01LClassical Simulation of (Bio)Molecular Systems Information W6 credits4GP. H. Hünenberger, J. Dolenc, S. Riniker
AbstractMolecular models, classical force fields, configuration sampling, molecular dynamics simulation, boundary conditions, electrostatic interactions, analysis of trajectories, free-energy calculations, structure refinement, applications in chemistry and biology. Exercises: hands-on computer exercises for learning progressively how to perform an analyze classical simulations (using the package GROMOS).
ObjectiveIntroduction to classical (atomistic) computer simulation of (bio)molecular systems, development of skills to carry out and interpret these simulations.
ContentMolecular models, classical force fields, configuration sampling, molecular dynamics simulation, boundary conditions, electrostatic interactions, analysis of trajectories, free-energy calculations, structure refinement, applications in chemistry and biology. Exercises: hands-on computer exercises for learning progressively how to perform an analyze classical simulations (using the package GROMOS).
Lecture notesThe powerpoint slides of the lectures will be made available weekly on the website in pdf format (on the day preceding each lecture).
LiteratureSee: Link
Prerequisites / NoticeSince the exercises on the computer do convey and test essentially different skills than those being conveyed during the lectures and tested at the oral exam, the results of the exercises are taken into account when evaluating the results of the exam (learning component, possible bonus of up to 0.25 points on the exam mark).

For more information about the lecture: Link
Biosystems
NumberTitleTypeECTSHoursLecturers
636-0007-00LComputational Systems Biology Information W6 credits3V + 2UJ. Stelling
AbstractStudy of fundamental concepts, models and computational methods for the analysis of complex biological networks. Topics: Systems approaches in biology, biology and reaction network fundamentals, modeling and simulation approaches (topological, probabilistic, stoichiometric, qualitative, linear / nonlinear ODEs, stochastic), and systems analysis (complexity reduction, stability, identification).
ObjectiveThe aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks.
ContentBiology has witnessed an unprecedented increase in experimental data and, correspondingly, an increased need for computational methods to analyze this data. The explosion of sequenced genomes, and subsequently, of bioinformatics methods for the storage, analysis and comparison of genetic sequences provides a prominent example. Recently, however, an additional area of research, captured by the label "Systems Biology", focuses on how networks, which are more than the mere sum of their parts' properties, establish biological functions. This is essentially a task of reverse engineering. The aim of this course is to provide an introductory overview of corresponding computational methods for the modeling, simulation and analysis of biological networks. We will start with an introduction into the basic units, functions and design principles that are relevant for biology at the level of individual cells. Making extensive use of example systems, the course will then focus on methods and algorithms that allow for the investigation of biological networks with increasing detail. These include (i) graph theoretical approaches for revealing large-scale network organization, (ii) probabilistic (Bayesian) network representations, (iii) structural network analysis based on reaction stoichiometries, (iv) qualitative methods for dynamic modeling and simulation (Boolean and piece-wise linear approaches), (v) mechanistic modeling using ordinary differential equations (ODEs) and finally (vi) stochastic simulation methods.
Lecture notesLink
LiteratureU. Alon, An introduction to systems biology. Chapman & Hall / CRC, 2006.

Z. Szallasi et al. (eds.), System modeling in cellular biology. MIT Press, 2010.

B. Ingalls, Mathematical modeling in systems biology: an introduction. MIT Press, 2013
636-0706-00LSpatio-Temporal Modelling in Biology Information W4 credits3GD. Iber
AbstractThis course focuses on modeling spatio-temporal problems in biology, in particular on the cell and tissue level. The main focus is on mechanisms and concepts, but mathematical and numerical techniques are introduced as required. Biological examples discussed in the course provide an introduction to key concepts in developmental biology.
ObjectiveStudents will learn state-of-the-art approaches to modelling spatial effects in dynamical biological systems. The course provides an introduction to dynamical system, and covers the mathematical analysis of pattern formation in growing, developing systems, as well as the description of mechanical effects at the cell and tissue level. The course also provides an introduction to image-based modelling, i.e. the use of microscopy data for model development and testing. The course covers classic as well as current approaches and exposes students to open problems in the field. In this way, the course seeks to prepare students to conduct research in the field. The course prepares students for research in developmental biology, as well as for applications in tissue engineering, and for biomedical research.
Content1. Introduction to Modelling in Biology
2. Bioimage Analysis
3. Morphogen Gradients
4. Precision & Robustness of Patterning
5. Mathematical Description of Growing Biological Systems
6. Travelling Waves & Wave Pinning
7. Turing Patterns
8. Chemotaxis
9. Epithelial Organisation
10. Tissue Simulation Frameworks
11. Tissue Mechanics & Fluid Dynamics
12. Growth Control
13. Image-Based Modelling
14. Summary
Lecture notesAll lecture material will be made available online via Moodle.
LiteratureThe lecture course is not based on any textbook. The following textbooks are related to some of its content. The textbooks may be of interest for further reading, but are not necessary to follow the course:

Murray, Mathematical Biology, Springer
Forgacs and Newman, Biological Physics of the Developing Embryo, CUP
Keener and Sneyd, Mathematical Physiology, Springer
Fall et al, Computational Cell Biology, Springer
Szallasi et al, System Modeling in Cellular Biology, MIT Press
Wolkenhauer, Systems Biology
Kreyszig, Engineering Mathematics, Wiley
Prerequisites / NoticeThe course is self-contained. The course assumes no background in biology but a good foundation regarding mathematical and computational techniques.
636-0117-00LMathematical Modelling for Bioengineering and Systems Biology Information W4 credits3GD. Iber
AbstractBasic concepts and mathematical tools to explore biochemical reaction kinetics and biological network dynamics.
ObjectiveThe course enables students to formulate, analyse, and simulate mathematical models of biochemical networks. To this end, the course covers basic mathematical concepts and tools to explore biochemical reaction dynamics as well as basic concepts from dynamical systems theory. The exercises serve to deepen the understanding of the presented concepts and the mathematical methods, and to train students to numerically solve and simulate mathematical models.
ContentBiochemical Reaction Modelling
Basic Concepts from Linear Algebra & Differential Equations Mathematical Methods: Linear Stability Analysis, Phase Plane Analysis, Bifurcation Analysis Dynamical Systems: Switches, Oscillators, Adaptation Signal Propagation in Signalling Networks Parameter Estimation
Data Science
NumberTitleTypeECTSHoursLecturers
636-0018-00LData Mining IW6 credits3G + 2AK. M. Borgwardt
AbstractData Mining, the search for statistical dependencies in large databases, is of utmost important in modern society, in particular in biological and medical research. This course provides an introduction to the key problems, concepts, and algorithms in data mining, and the applications of data mining in computational biology.
ObjectiveThe goal of this course is that the participants gain an understanding of data mining problems and algorithms to solve these problems, in particular in biological and medical applications.
ContentThe goal of the field of data mining is to find patterns and statistical dependencies in large databases, to gain an understanding of the underlying system from which the data were obtained. In computational biology, data mining contributes to the analysis of vast experimental data generated by high-throughput technologies, and thereby enables the generation of new hypotheses.

In this course, we will present the algorithmic foundations of data mining and its applications in computational biology. The course will feature an introduction to popular data mining problems and algorithms, reaching from classification via clustering to feature selection. This course is intended for both students who are interested in applying data mining algorithms and students who would like to gain an understanding of the key algorithmic concepts in data mining.

Tentative list of topics:

1. Distance functions
2. Classification
3. Clustering
4. Feature Selection
Lecture notesCourse material will be provided in form of slides.
LiteratureWill be provided during the course.
Prerequisites / NoticeBasic understanding of mathematics, as taught in basic mathematics courses at the Bachelor's level.
252-0535-00LAdvanced Machine Learning Information W10 credits3V + 2U + 4AJ. M. Buhmann, C. Cotrini Jimenez
AbstractMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
ObjectiveStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
ContentThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
Lecture notesNo lecture notes, but slides will be made available on the course webpage.
LiteratureC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Prerequisites / NoticeThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
636-0101-00LSystems GenomicsW4 credits3GB. Treutlein, C. Beisel, Z. He
AbstractThis course is an introduction to the wide field of Genomics. It addresses how fundamental questions in biological systems are studied using methods in genomics and how the resulting data is analysed to make quantitative interpretations of biological phenomena.
ObjectiveThe goal of this course is to get detailed insights in how state-of-the-art DNA sequencing technologies can be applied for a qualitative and quantitative description of molecular and cellular processes and function. Students will learn how to analyse RNA-seq / transcriptomics data and make biological interpretations in a quantitative manner.
ContentThis course will be a mix of lecture sessions, hands-on computational data analysis using public datasets and seminars discussing own results in the context of the published studies. In the lectures we will introduce current Next-Generation Sequencing technologies and their application to address basically all facets of modern biology and biomedical research. We will cover the major sample processing methods used for investigating functional genomic aspects like transcriptome and chromatin profiling, review recent advances in (cancer) genome sequencing and give an overview of public big data sequencing projects (ENCODE, GTEX, TCGA, ...).
For the computational data analysis we will focus on differential gene expression profiling (RNA-seq) experiments that have been selected from fascinating published biological studies. Data analysis based on R will follow a detailed tutorial describing all required steps of sequence read processing and will be conducted in small groups to enable every student hands-on experience.
Lecture notesThe PowerPoint presentations of the lectures as well as other course material relevant for an active participation will be made available online.
Seminar
Compulsory seminar.
NumberTitleTypeECTSHoursLecturers
636-0704-00LComputational Biology and Bioinformatics Seminar Restricted registration - show details
Number of participants limited to 30
The seminar is addressed primarily at students enrolled in the MSc CBB programme.
Students of other ETH study programmes interested in this course need to ask the lecturer for permission to enrol in the course.

The Seminar will be offered in autumn semester in Basel (involving professors and lecturers from the University of Basel) and in spring semester in Zurich (involving professors and lecturers from the University of Zurich). Professors and lecturers from ETH Zurich are involved in both semesters.
O2 credits2SN. Beerenwinkel, K. M. Borgwardt, D. Iber, M. H. Khammash, T. Stadler, J. Stelling
AbstractComputational biology and bioinformatics aim at an understanding of living systems through computation. The seminar combines student presentations and current research project presentations to review the rapidly developing field from a computer science perspective. Areas: DNA sequence analysis, proteomics, optimization and bio-inspired computing, and systems modeling, simulation and analysis.
ObjectiveStudying and presenting fundamental papers of Computational Biology and Bioinformatics. Learning how to make a scientific presentation and how classical methods are used or further developed in current research.
ContentComputational biology and bioinformatics aim at advancing the understanding of living systems through computation. The complexity of these systems, however, provides challenges for software and algorithms, and often requires entirely novel approaches in computer science. The aim of the seminar is to give an overview of this rapidly developing field from a computer science perspective. In particular, it will focus on the areas of (i) DNA sequence analysis, sequence comparison and reconstruction of phylogenetic trees, (ii) protein identification from experimental data, (iii) optimization and bio-inspired computing, and (iv) systems analysis of complex biological networks. The seminar combines the discussion of selected research papers with a major impact in their domain by the students with the presentation of current active research projects / open challenges in computational biology and bioinformatics by the lecturers. Each week, the seminar will focus on a different topic related to ongoing research projects at ETHZ, University of Basel and University of Zurich, thus giving the students the opportunity of obtaining knowledge about the basic research approaches and problems as well as of gaining insight into (and getting excited about) the latest developments in the field.
LiteratureOriginal papers to be presented by the students will be provided in the first week of the seminar.
Advanced Courses
A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof at least 16 ECTS in the Theory and at least 10 ECTS in the Biology category.
Note that some of the lectures are being recorded: Link
Theory
At least 16 ECTS need to be acquired in this category.
NumberTitleTypeECTSHoursLecturers
401-0663-00LNumerical Methods for Computer Science Information W7 credits2V + 2U + 2PR. Hiptmair
AbstractThe course gives an introduction into fundamental techniques and algorithms of numerical mathematics which play a central role in numerical simulations in science and technology. The course focuses on fundamental ideas and algorithmic aspects of numerical methods. The exercises involve actual implementation of numerical methods in C++.
Objective* Knowledge of the fundamental algorithms in numerical mathematics
* Knowledge of the essential terms in numerical mathematics and the
techniques used for the analysis of numerical algorithms
* Ability to choose the appropriate numerical method for concrete problems
* Ability to interpret numerical results
* Ability to implement numerical algorithms afficiently
ContentFirst two weeks: A gentle introduction to C++

1. Computing with Matrices and Vectors
1.1 Fundamentals
1.2 Software and Libraries
1.4 Computational Effort
1.5 Machine Arithmetic and Consequences

2. Direct Methods for (Square) Linear Systems of Equations
2.1 Introduction: Linear Systems of Equations
2.3 Gaussian Elimination
2.6 Exploiting Structure when Solving Linear Systems
2.7 Sparse Linear Systems

3. Direct Methods for Linear Least Squares Problems
3.1 Least Squares Solution Concepts
3.2 Normal Equation Methods
3.3 Orthogonal Transformation Methods
3.3.1 Transformation Idea
3.3.2 Orthogonal/Unitary Matrices
3.3.3 QR-Decomposition
3.3.4 QR-Based Solver for Linear Least Squares Problems
3.4 Singular Value Decomposition

4. Filtering Algorithms
4.1 Filters and Convolutions
4.2 Discrete Fourier Transform (DFT)
4.3 Fast Fourier Transform (FFT)

5. Machine Learning of One-Dimensional Data
(Data Interpolation and Data Fitting in 1D)
5.1 Abstract Interpolation (AI)
5.2 Global Polynomial Interpolation

8. Iterative Methods for Non-Linear Systems of Equations
8.1 Introduction
8.2 Iterative Methods
8.3 Fixed-Point Iterations
8.4 Finding Zeros of Scalar Functions
8.5 Newton’s Method in Rn
8.6. Quasi-Newton Method
Lecture notesLecture materials (PDF documents and codes) will be made available to the participants through the course web page and online repositories. Access information will be communicated in the beginning of the course.
LiteratureU. ASCHER AND C. GREIF, A First Course in Numerical Methods, SIAM, Philadelphia, 2011.

A. QUARTERONI, R. SACCO, AND F. SALERI, Numerical mathematics, vol. 37 of Texts in Applied Mathematics, Springer, New York, 2000.

W. Dahmen, A. Reusken "Numerik für Ingenieure und Naturwissenschaftler", Springer 2006.

W. Gander, M.J. Gander, and F. Kwok "Scientific Computing", Springer 2014.

M. Hanke-Bourgeois "Grundlagen der Numerischen Mathematik und des wissenschaftlichen Rechnens", BG Teubner, 2002

P. Deuflhard and A. Hohmann, "Numerische Mathematik I", DeGruyter, 2002
Prerequisites / NoticeThe course will be accompanied by programming exercises in C++ relying on the template library EIGEN. Familiarity with C++, object oriented and generic programming is an advantage. Participants of the course are expected to learn C++ by themselves, in case they do not know it already.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Decision-makingfostered
Problem-solvingassessed
Project Managementfostered
263-5210-00LProbabilistic Artificial Intelligence Information Restricted registration - show details W8 credits3V + 2U + 2AA. Krause
AbstractThis course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics.
ObjectiveHow can we build systems that perform well in uncertain environments? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as robotics. The course is designed for graduate students.
ContentTopics covered:
- Probability
- Probabilistic inference (variational inference, MCMC)
- Bayesian learning (Gaussian processes, Bayesian deep learning)
- Probabilistic planning (MDPs, POMPDPs)
- Multi-armed bandits and Bayesian optimization
- Reinforcement learning
Prerequisites / NoticeSolid basic knowledge in statistics, algorithms and programming.
The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.
401-0647-00LIntroduction to Mathematical OptimizationW5 credits2V + 1UD. Adjiashvili
AbstractIntroduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.
ObjectiveThe goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering.
ContentTopics covered in this course include:
- Linear programming (simplex method, duality theory, shadow prices, ...).
- Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...).
- Modelling with mathematical optimization: applications of mathematical programming in engineering.
LiteratureInformation about relevant literature will be given in the lecture.
Prerequisites / NoticeThis course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications.
227-0225-00LLinear System TheoryW6 credits5GJ. Lygeros, A. Tsiamis
AbstractThe class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems.
ObjectiveStudents should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.
Content- Proof techniques and practices.
- Linear spaces, normed linear spaces and Hilbert spaces.
- Ordinary differential equations, existence and uniqueness of solutions.
- Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case.
- Controllability and observability, duality. Time invariant systems treated as a special case.
- Stability and stabilization, observers, state and output feedback, separation principle.
Lecture notesAvailable on the course Moodle platform.
Prerequisites / NoticeSufficient mathematical maturity, in particular in linear algebra, analysis.
CompetenciesCompetencies
Subject-specific CompetenciesConcepts and Theoriesassessed
Techniques and Technologiesassessed
Method-specific CompetenciesAnalytical Competenciesassessed
Problem-solvingassessed
Personal CompetenciesCreative Thinkingfostered
Critical Thinkingfostered
Integrity and Work Ethicsfostered
151-0575-01LSignals and Systems Information W4 credits2V + 2UA. Carron
AbstractSignals arise in most engineering applications. They contain information about the behavior of physical systems. Systems respond to signals and produce other signals. In this course, we explore how signals can be represented and manipulated, and their effects on systems. We further explore how we can discover basic system properties by exciting a system with various types of signals.
ObjectiveMaster the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise.
ContentDiscrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design.
Lecture notesLecture notes available on course website.
Prerequisites / NoticeControl Systems I is helpful but not required.
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