Search result: Catalogue data in Spring Semester 2020
|Computer Science Bachelor|
|First Year Examinations|
| First Year Examination Block 1|
Offered in the autumn semester.
|First Year Examination Block 2|
|401-0212-16L||Analysis I||O||7 credits||4V + 2U||Ö. Imamoglu|
|Abstract||Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable|
|Objective||Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable|
|Content||Real and complex numbers, vectors, functions, limits, sequences, series, power series, differentiation and integration in one variable|
|Literature||Michael Struwe: Analysis für Informatik|
Christian Blatter: Ingenieur-analysis
Tom Apostol: Mathematical Analysis
Teaching materials and further information is available through the course website (https://metaphor.ethz.ch/x/2018/fs/401-0212-16L/)
|252-0028-00L||Digital Design and Computer Architecture||O||7 credits||4V + 2U||O. Mutlu, F. K. Gürkaynak|
|Abstract||The class provides a first introduction to the design of digital circuits and computer architecture. It covers technical foundations of how a computing platform is designed from the bottom up. It introduces various execution paradigms, hardware description languages, and principles in digital design and computer architecture.|
|Objective||This class provides a first approach to Computer Architecture. The students learn the design of digital circuits in order to:|
- understand the basics,
- understand the principles (of design),
- understand the precedents (in computer architecture).
Based on such understanding, the students are expected to:
- learn how a modern computer works underneath, from the bottom up,
- evaluate tradeoffs of different designs and ideas,
- implement a principled design (a simple microprocessor),
- learn to systematically debug increasingly complex systems,
- hopefully be prepared to develop novel, out-of-the-box designs.
The focus is on basics, principles, precedents, and how to use them to create/implement good designs.
|Content||The class consists of the following major blocks of contents:|
- Major Current Issues in Computer Architecture: Principles, Mysteries, Motivational Case Studies and Examples
- Digital Logic Design: Combinational Logic, Sequential Logic, Hardware Description Languages, FPGAs, Timing and Verification.
- Basics of Computer Architecture: Von Neumann Model of Computing, Instruction Set Architecture, Assembly Programming, Microarchitecture, Microprogramming.
- Basics of Processor Design: Pipelining, Out-of-Order Execution, Branch Prediction.
- Execution Paradigms: Out-of-order Execution, Dataflow, Superscalar Execution, VLIW, SIMD Processors, GPUs, Systolic Arrays, Multithreading.
- Memory System: Memory Organization, Memory Technologies, Memory Hierarchy, Caches, Virtual Memory.
|Lecture notes||All the materials (including lecture slides) will be provided on the course website: |
The video recordings of the lectures are likely to be made available after lectures, but there may be delays associated with the posting of online videos.
|Literature||Patt and Patel's "Introduction to Computing Systems" and Harris and Harris's "Digital Design and Computer Architecture" are the official textbooks of the course.|
We will provide required and recommended readings in every lecture since the course is cutting-edge and there is no textbook that covers what the course covers. They will be mostly chapters of the two textbooks, and important articles that are essential for understanding the material.
|252-0029-00L||Parallel Programming||O||7 credits||4V + 2U||T. Hoefler, H. Lehner, M. Schwerhoff|
|Abstract||Introduction to parallel programming: deterministic and non-deterministic programs, models for parallel computation, synchronization, communication, and fairness.|
|Objective||The student should learn how to write a correct parallel program, how to measure its efficiency, and how to reason about a parallel program. Student should become familiar with issues, problems, pitfalls, and solutions related to the construction of parallel programs. Labs provide an opportunity to gain experience with threads, libraries for thread management in modern programming lanugages (e.g., Java, C#) and with the execution of parallel programs on multi-processor/multi-core computers.|
|252-0030-00L||Algorithms and Probability||O||7 credits||4V + 2U||A. Steger, E. Welzl|
|Abstract||Es werden klassische Algorithmen aus verschiedenen Anwendungsbereichen vorgestellt. In die diskrete Wahrscheinlichkeitstheorie wird eingeführt und das Konzept randomisierter Algorithmen an verschiedenen Beispielen vorgestellt.|
|Objective||Verständnis des Entwurfs und der Analyse von Algorithmen. Grundlagen der diskreten Wahrscheinlichkeitstheorie und ihrer Anwendung in der Algorithmik.|
|Content||Fortsetzung der Vorlesung Algorithmen und Datenstrukturen des ersten Semesters.|
|252-0058-00L||Formal Methods and Functional Programming||O||7 credits||4V + 2U||P. Müller, D. Traytel|
|Abstract||In this course, participants will learn about new ways of specifying, reasoning about, and developing programs and computer systems. The first half will focus on using functional programs to express and reason about computation. The second half presents methods for developing and verifying programs represented as discrete transition systems.|
|Objective||In this course, participants will learn about new ways of specifying,|
reasoning about, and developing programs and computer systems. Our objective is to help students raise their level of abstraction in modeling and implementing systems.
|Content||The first part of the course will focus on designing and reasoning|
about functional programs. Functional programs are mathematical
expressions that are evaluated and reasoned about much like ordinary
mathematical functions. As a result, these expressions are simple to
analyze and compose to implement large-scale programs. We will cover the mathematical foundations of functional programming, the lambda calculus, as well as higher-order programming, typing, and proofs of correctness.
The second part of the course will focus on deductive and algorithmic validation of programs modeled as transition systems. As an example of deductive verification, students will learn how to formalize the semantics of imperative programming languages and how to use a formal semantics to prove properties of languages and programs. As an example of algorithmic validation, the course will introduce model checking and apply it to programs and program designs.
|252-0063-00L||Data Modelling and Databases||O||7 credits||4V + 2U||C. Zhang|
|Abstract||Data modelling (Entity Relationship), relational data model, relational design theory (normal forms), SQL, database integrity, transactions and advanced database engines|
|Objective||Introduction to relational databases and data management. Basics of SQL programming and transaction management.|
|Content||The course covers the basic aspects of the design and implementation of databases and information systems. The courses focuses on relational databases as a starting point but will also cover data management issues beyond databases such as: transactional consistency, replication, data warehousing, other data models, as well as SQL.|
|Literature||Kemper, Eickler: Datenbanksysteme: Eine Einführung. Oldenbourg Verlag, 7. Auflage, 2009.|
Garcia-Molina, Ullman, Widom: Database Systems: The Complete Book. Pearson, 2. Auflage, 2008.
|252-0064-00L||Computer Networks||O||7 credits||4V + 2U||A. Perrig, A. Singla|
|Abstract||This introductory course on computer networking takes a top-down view from networked applications all through the physical layer.|
|Objective||Students will get a comprehensive overview of the key protocols and the architecture of the Internet, as one example of more general principles in network design. Students will also acquire hands-on experience in programming different aspects of a computer networks. Apart from the state-of-the-art in networking practice, students will explore the rationale for the design choices that networks in the past have made, and where applicable, why these choices may no longer be ideal.|
|Lecture notes||The slides for each lecture will be made available through the course Web page, along with additional reference material.|
|Literature||Computer Networking: A Top-Down Approach, James F. Kurose and Keith W. Ross. Pearson; 7th edition (May 6, 2016)|
|Prerequisites / Notice||The bonus projects use C programming. ETH courses in the Bachelor track before this course already cover this. For other students, e.g., exchange, please take note of this requirement: you can still take the course and get a good (even 6/6) grade, but you are disadvantaged compared to others who can get the bonus points, if you don't fulfill this prerequisite.|
|401-0614-00L||Probability and Statistics||O||5 credits||2V + 2U||J. Teichmann|
|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/2019/fs/401-0614-00L/|
|Major: Systems and Software Engineering|
|252-0216-00L||Rigorous Software Engineering||O||8 credits||4V + 2U + 1A||F. Friedrich Wicker, H. Lehner, M. Schwerhoff|
|Abstract||This course introduces both theoretical and applied aspects of software engineering and analysis. It covers:|
- Software Architecture
- Informal and formal Modeling
- Design Patterns
- Code Refactoring
- Program Testing
- Dynamic Program Analysis
- Static Program Analysis
|Objective||The course has two main objectives:|
- Understand, end-to-end (theoretical and practical), the core techniques for building quality software
- Understand how to apply these techniques in practice
|Content||Some of the core technical topics covered will be:|
- modeling and mapping of models to code
- common code design patterns
- functional and structural testing
- dynamic and static analysis
|Literature||Will be announced in the lecture.|
|Major: Information and Data Processing|
|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 firstname.lastname@example.org
|O||8 credits||4V + 2U + 1A||A. Krause|
|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"
|Major: Theoretical Computer Science|
|252-0211-00L||Information Security||O||8 credits||4V + 3U||D. Basin, S. Capkun, R. Sasse|
|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
Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses.
|252-0055-00L||Information Theory||W||4 credits||2V + 1U||L. Haug, J. M. Buhmann|
|Abstract||The course covers the fundamental concepts of Shannon's information theory. |
The most important topics are: Entropy, information, data compression, channel coding, codes.
|Objective||The goal of the course is to familiarize with the theoretical fundamentals of information theory and to illustrate the practical use of the theory with the help of selected examples of data compression and coding.|
|Content||Introduction and motivation, basics of probability theory, entropy and information, Kraft inequality, bounds on expected length of source codes, Huffman coding, asymptotic equipartition property and typical sequences, Shannon's source coding theorem, channel capacity and channel coding, Shannon's noisy channel coding theorem, examples|
|Literature||T. Cover, J. Thomas: Elements of Information Theory, John Wiley, 1991.|
D. MacKay, Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.
C. Shannon, The Mathematical Theory of Communication, 1948.
|252-0820-00L||Case Studies from Practice||W||4 credits||2V + 1U||M. Brandis|
|Abstract||The course is designed to provide students with an understanding of "real-life" computer science challenges in business settings and teach them how to address these.|
|Objective||By using case studies that are based on actual IT projects, students will learn how to deal with complex, not straightforward problems. It will help them to apply their theoretical Computer Science background in practice and will teach them fundamental principles of IT management and challenges with IT in practice.|
A particular focus is to make the often imprecise and fuzzy problems in practice accessible to factual analysis and reasoning, and to challenge "common wisdom" and hearsay.
|Content||The course consists of multiple lectures on methods to systematically analyze problems in a business setting and communicate about them as well as about IT management and IT economics, presented by the lecturer, and a number of case studies provided by guest lecturers from either IT companies or IT departments of a diverse range of companies. Students will obtain insights into both established and startup companies, small and big, and different industries.|
Presenting companies have included avaloq, Accenture, AdNovum, Bank Julius Bär, Credit Suisse, Deloitte, HP, Hotelcard, IBM Research, McKinsey & Company, Open Web Technology, SAP Research, Selfnation, SIX Group, Teralytics, 28msec, Zühlke and dormakaba, and Marc Brandis Strategic Consulting. The participating companies in spring 2019 will be announced at course start.
|Prerequisites / Notice||Participants should be aware that the provided documents supporting the cases are usually taken directly from the projects and companies being addressed, and thus differ very much in terms of presentation style, terminology, and explicitly provided contextual information.|
Earlier participants have found it difficult to solve the exercises completely and to fully grasp the contents taught in the cases, if they were not able to attend the case presentation, and were just relying on the provided documents.
|151-0116-10L||High Performance Computing for Science and Engineering (HPCSE) for Engineers II||W||4 credits||4G||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 Uncertainty Quantification and Propagation including the implementation of relevant 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
|Content||High Performance Computing:|
- Advanced topics in shared-memory programming
- Advanced topics in MPI
- GPU architectures and CUDA programming
- Uncertainty quantification under parametric and non-parametric modeling uncertainty
- Bayesian inference with model class assessment
- Markov Chain Monte Carlo simulation
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||Students must be familiar with the content of High Performance Computing for Science and Engineering I (151-0107-20L)|
|401-0674-00L||Numerical Methods for Partial Differential Equations|
Not meant for BSc/MSc students of mathematics.
|W||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
18.104.22.168 L EHR FEM++ Mesh: Container Layer
22.214.171.124 L EHR FEM++ Mesh: Topology Layer
126.96.36.199 L EHR FEM++ Mesh: Geometry Layer
2.7.3 Vectors and Matrices
2.7.4 Assembly Algorithms
188.8.131.52 Assembly: Localization
184.108.40.206 Assembly: Index Mappings
220.127.116.11 Distribute Assembly Schemes
18.104.22.168 Assembly: Linear Algebra Perspective
2.7.5 Local Computations
22.214.171.124 Analytic Formulas for Entries of Element Matrices
126.96.36.199 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.
|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
|227-0075-00L||Electrical Engineering I||W||3 credits||2V + 2U||J. Leuthold|
|Abstract||Basic course in electrical engineering with the following topics: Concepts of voltage and currents; Analyses of dc and ac networks; Series and parallel resistive circuits, circuits including capacitors and inductors; Kirchhoff's laws and other network theorems; Transient responses; Basics of electrical and magnetic fields;|
|Objective||Understanding of the basic concepts in electrical engineering with focus on network theory. The successful student knows the basic components of electrical circuits and the network theorems after attending the course.|
|Content||Diese Vorlesung vermittelt Grundlagenkenntnisse im Fachgebiet Elektrotechnik. Ausgehend von den grundlegenden Konzepten der Spannung und des Stroms wird die Analyse von Netzwerken bei Gleich- und Wechselstrom behandelt. Dabie werden folgende Themen behandelt:|
Kapitel 1 Das elektrostatische Feld
Kapitel 2 Das stationäre elektrische Strömungsfeld
Kapitel 3 Einfache elektrische Netzwerke
Kapitel 4 Halbleiterbauelemente (Dioden, der Transistor)
Kapitel 5 Das stationäre Magnetfeld
Kapitel 6 Das zeitlich veränderliche elektromagnetische Feld
Kapitel 7 Der Übergang zu den zeitabhängigen Strom- und Spannungsformen
Kapitel 8 Wechselspannung und Wechselstrom
|Lecture notes||Die Vorlesungsfolien werden auf Moodle bereitgestellt.|
Als ausführliches Skript wird das Buch "Manfred Albach. Elektrotechnik, Person Verlag, Ausgabe vom 1.8.2011" empfohlen.
|Literature||Für das weitergehende Studium werden in der Vorlesung verschiedene Bücher vorgestellt.|
|227-0123-00L||Mechatronics||W||6 credits||4G||T. M. Gempp|
|Abstract||Introduction into mechatronics. Sensors and actors. Electronic and hydraulic power amplifiers. Data processing and basics of real-time programming, multitasking, and multiprocessing. Modeling of mechatronical systems. Geometric, kinematical, and dynamic elements. Fundamentals of the systems theory. Examples from industrial applications.|
|Objective||Introduction into the basics and technology of mechatronical devices. Theoretical and practical know-how of the basic elements of a mechatronical system.|
|Content||Introduction into mechatronics. Sensors and actors. Electronic and hydraulic power amplifiers. Data processing and basics of real-time programming, multitasking, and multiprocessing. Modeling of mechatronical systems. Geometric, kinematical, and dynamic elements. Fundamentals of the systems theory. Examples from industrial applications.|
|Lecture notes||Recommendation of textbook. Additional documentation to the individual topics. Documentation from industrial companies.|
|227-0707-00L||Optimization Methods for Engineers||W||3 credits||2G||P. Leuchtmann|
|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.|
|227-0803-00L||Energy, Resources, Environment: Risks and Prospects||W||6 credits||4G||O. Zenklusen, T. Flüeler|
|Abstract||Multidisciplinary, interactive course focussing on current debates around environmental and energy issues. Topics include: energy transition, nuclear energy and climate change, 2000-Watt-Society. Concepts such as risk, sustainable development and eco-efficiency are applied to case studies. The course is designed for a pluridisciplinary audience and provides a training ground for critical thinking.|
|Objective||Develop capacities for explicating environmental problems, for scrutinising proposed solutions and for contributing to debates. Analyse complex issues from different perspectives and using a variety of analytical concepts. Understand interactions between the environment, science and technology, society and the economy. Develop skills in critical thinking, scientific writing and presenting.|
|Content||Following a multidisciplinary outline of current issues in environmental and energy policy, the course introduces theoretical and analytical approaches including "risk", "sustainability", "resource management", "messy problems" as well as concepts from institutional design and environmental economics. Large parts of the course are dedicated to case studies and contributions from participants. These serve for applying concepts to concrete challenges and debates. Topics may include: energy transition, innovation, carbon markets, the future of nuclear energy, climate change and development policy, dealing with disaster risk, the use of non-renewable resources, as well as visions such as 2000-watt society.|
|Lecture notes||Presentations and reader provided in electronic formats.|
|Literature||Reader provided in electronic formats.|
|Prerequisites / Notice||-|
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