Search result: Catalogue data in Autumn Semester 2023
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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252-0025-01L | Discrete Mathematics ![]() | O | 7 credits | 4V + 2U | U. Maurer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Content: Mathematical reasoning and proofs, abstraction. Sets, relations (e.g. equivalence and order relations), functions, (un-)countability, number theory, algebra (groups, rings, fields, polynomials, subalgebras, morphisms), logic (propositional and predicate logic, proof calculi). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The primary goals of this course are (1) to introduce the most important concepts of discrete mathematics, (2) to understand and appreciate the role of abstraction and mathematical proofs, and (3) to discuss a number of applications, e.g. in cryptography, coding theory, and algorithm theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | See course description. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | available (in english) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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252-0027-00L | Introduction to Programming ![]() ![]() | O | 7 credits | 4V + 2U | T. Gross | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Introduction to fundamental concepts of modern programming and operational skills for developing high-quality programs, including large programs as in industry. The course introduces software engineering principles with an object-oriented approach based. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Many people can write programs. The "Introduction to Programming" course goes beyond that basic goal: it teaches the fundamental concepts and skills necessary to perform programming at a professional level. As a result of successfully completing the course, students master the fundamental control structures, data structures, reasoning patterns and programming language mechanisms characterizing modern programming, as well as the fundamental rules of producing high-quality software. They have the necessary programming background for later courses introducing programming skills in specialized application areas. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Basics of object-oriented programming. Objects and classes. Pre- and postconditions, class invariants, design by contract. Fundamental control structures. Assignment and references. Fundamental data structures and algorithms. Recursion. Inheritance and interfaces, basic concepts of Software Engineering such as the software process, specification and documentation, debugging, reuse and quality assurance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | The lecture slides are available for download on the course page. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | See the course page for up-to-date information. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | There are no special prerequisites. Students are expected to enroll in the other courses offered to first-year students of computer science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-0026-00L | Algorithms and Data Structures ![]() | O | 7 credits | 3V + 2U + 1A | J. Lengler, D. Steurer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course provides the foundation of the design and analysis of algorithms. The material is introduced using classical algorithmic problems including graph problems. The necessary basic introduction to graph theory is provided as part of this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | An understanding of the design and analysis of fundamental algorithms and data structures. A basic understanding of graph theory and several basic graph algorithms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This course is an introduction into the design and analysis of algorithms. On the one hand this includes classical algorithm design patterns including induction, divide-and-conquer and dynamic programming. We study these using classical example such as searching and sorting. On the other hand the course covers the interaction between algorithms and data structures including linked lists, search trees, heaps, and union-find structures. A particular focus are graph algorithms for shortest path and minimal spanning tree problems. We provide the necessary introduction into graph theory as part of this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | A complete script in German is under development. A complete draft is already available on the course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Abgesehen vom Skript und Vorlesungsunterlagen empfehlen wir die folgenden Bücher als zusätzliches Nachschlagewerk. Th. Ottmann, P. Widmayer: Algorithmen und Datenstrukturen, Spektrum-Verlag, 5. Auflage, Heidelberg, Berlin, Oxford, 2011 Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein: An Introduction to Algorithms, 3rd edition, MIT Press, 2009 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-0131-00L | Linear Algebra ![]() | O | 7 credits | 4V + 2U | A. Bandeira, B. Gärtner | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Introduction to linear algebra: vectors and matrices, solving systems of linear equations, vector spaces and subspaces, orthogonality and least squares, determinants, eigenvalues and eigenvectors, singular value decomposition and linear transformations. Applications in and links to computer science will be presented in parallel. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | - Understand and apply fundamental concepts of linear algebra - Learn about applications of linear algebra in computer science | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Vectors and matrices, solving systems of linear equations, vector spaces and subspaces, orthogonality and least squares, determinants, eigenvalues and eigenvectors, singular value decomposition and linear transformations. Applications in and links to computer science. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Gilbert Strang, Introduction to Linear Algebra, 6th Edition, Wellesley - Cambridge Press. Further literature and links can be found on the course webpage. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-0057-00L | Theoretical Computer Science ![]() | O | 7 credits | 4V + 2U | D. Komm, H.‑J. Böckenhauer, J. Hromkovic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Concepts to cope with: a) what can be accomplished in a fully automated fashion (algorithmically solvable) b) How to measure the inherent difficulty of tasks (problems) c) What is randomness and how can it be useful? d) What is nondeterminism and what role does it play in CS? e) How to represent infinite objects by finite automata and grammars? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Learning the basic concepts of computer science along their historical development | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This lecture gives an introduction to theoretical computer science, presenting the basic concepts and methods of computer science in its historical context. We present computer science as an interdisciplinary science which, on the one hand, investigates the border between the possible and the impossible and the quantitative laws of information processing, and, on the other hand, designs, analyzes, verifies, and implements computer systems. The main topics of the lecture are: - alphabets, words, languages, measuring the information content of words, representation of algorithmic tasks - finite automata, regular and context-free grammars - Turing machines and computability - complexity theory and NP-completeness - design of algorithms for hard problems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | The lecture is covered in detail by the textbook "Theoretical Computer Science". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Basic literature: 1. J. Hromkovic: Theoretische Informatik. 5th edition, Springer Vieweg 2014. 2. J. Hromkovic: Theoretical Computer Science. Springer 2004. Further reading: 3. M. Sipser: Introduction to the Theory of Computation, PWS Publ. Comp.1997 4. J.E. Hopcroft, R. Motwani, J.D. Ullman: Introduction to Automata Theory, Languages, and Computation (3rd Edition), Addison-Wesley 2006. 5. I. Wegener: Theoretische Informatik. Teubner. More exercises and examples in: 6. A. Asteroth, Ch. Baier: Theoretische Informatik | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | During the semester, two non-obligatory test exams will be offered. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-0061-00L | Systems Programming and Computer Architecture ![]() | O | 7 credits | 4V + 2U | T. Roscoe, A. Klimovic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Introduction to systems programming. C and assembly language, floating point arithmetic, basic translation of C into assembler, compiler optimizations, manual optimizations. How hardware features like superscalar architecture, exceptions and interrupts, caches, virtual memory, multicore processors, devices, and memory systems function and affect correctness, performance, and optimization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The course objectives are for students to: 1. Develop a deep understanding of, and intuition about, the execution of all the layers (compiler, runtime, OS, etc.) between programs in high-level languages and the underlying hardware: the impact of compiler decisions, the role of the operating system, the effects of hardware on code performance and scalability, etc. 2. Be able to write correct, efficient programs on modern hardware, not only in C but high-level languages as well. 3. Understand Systems Programming as a complement to other disciplines within Computer Science and other forms of software development. This course does not cover how to design or build a processor or computer. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This course provides an overview of "computers" as a platform for the execution of (compiled) computer programs. This course provides a programmer's view of how computer systems execute programs, store information, and communicate. The course introduces the major computer architecture structures that have direct influence on the execution of programs (processors with registers, caches, other levels of the memory hierarchy, supervisor/kernel mode, and I/O structures) and covers implementation and representation issues only to the extend that they are necessary to understand the structure and operation of a computer system. The course attempts to expose students to the practical issues that affect performance, portability, security, robustness, and extensibility. This course provides a foundation for subsequent courses on operating systems, networks, compilers and many other courses that require an understanding of the system-level issues. Topics covered include: machine-level code and its generation by optimizing compilers, address translation, input and output, trap/event handlers, performance evaluation and optimization (with a focus on the practical aspects of data collection and analysis). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | - C programmnig - Integers - Pointers and dynamic memory allocation - Basic computer architecture - Compiling C control flow and data structures - Code vulnerabilities - Implementing memory allocation - Linking - Floating point - Optimizing compilers - Architecture and optimization - Caches - Exceptions - Virtual memory - Multicore - Devices | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | The course is based in part on "Computer Systems: A Programmer's Perspective" (3rd Edition) by R. Bryant and D. O'Hallaron, with additional material. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | 252-0029-00L Parallel Programming 252-0028-00L Design of Digital Circuits | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-0213-16L | Analysis II | O | 5 credits | 2V + 2U | L. Lewark | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Differential and Integral calculus in many variables, vector analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Literature | Für allgemeine Informationen, sehen Sie bitte die Webseite der Vorlesung | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-0663-00L | Numerical Methods for Computer Science ![]() | O | 7 credits | 2V + 2U + 2P | V. C. Gradinaru | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The 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++. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | First 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 notes | Lecture 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | U. 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 / Notice | The 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-0206-00L | Visual Computing ![]() | O | 8 credits | 4V + 3U | M. Gross, M. Pollefeys | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course acquaints students with core knowledge in computer graphics, image processing, multimedia and computer vision. Topics include: Graphics pipeline, perception and camera models, transformation, shading, global illumination, texturing, sampling, filtering, image representations, image and video compression, edge detection and optical flow. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | This course provides an in-depth introduction to the core concepts of computer graphics, image processing, multimedia and computer vision. The course forms a basis for the specialization track Visual Computing of the CS master program at ETH. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Course topics will include: Graphics pipeline, perception and color models, camera models, transformations and projection, projections, lighting, shading, global illumination, texturing, sampling theorem, Fourier transforms, image representations, convolution, linear filtering, diffusion, nonlinear filtering, edge detection, optical flow, image and video compression. In theoretical and practical homework assignments students will learn to apply and implement the presented concepts and algorithms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | A scriptum will be handed out for a part of the course. Copies of the slides will be available for download. We will also provide a detailed list of references and textbooks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Markus Gross: Computer Graphics, scriptum, 1994-2005 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-3110-00L | Human Computer Interaction ![]() | O | 8 credits | 3V + 2U + 2A | C. Holz, O. Hilliges | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course provides an introduction to the field of human-computer interaction, emphasising the central role of the user in system design. Through detailed case studies, students will be introduced to different methods used to analyse the user experience and shown how these can inform the design of new interfaces, systems and technologies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The goal of the course is that students should understand the principles of user-centred design and be able to apply these in practice. As well as understand the basic notions of Computational Design in a HCI context. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The course will introduce students to several methods of analysing the user experience, showing how these can be used at different stages of system development from requirements analysis through to usability testing. Students will get experience of designing and carrying out user studies as well as analysing results. The course will also cover the basic principles of interaction design. Practical exercises related to touch and gesture-based interaction will be used to reinforce the concepts introduced in the lecture. To get students to further think beyond traditional system design, we will discuss issues related to ambient information and awareness. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-0209-00L | Algorithms, Probability, and Computing ![]() | O | 8 credits | 4V + 2U + 1A | B. Gärtner, R. Kyng, A. Steger, D. Steurer, E. Welzl | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Advanced design and analysis methods for algorithms and data structures: Random(ized) Search Trees, Point Location, Minimum Cut, Linear Programming, Randomized Algebraic Algorithms (matchings), Probabilistically Checkable Proofs (introduction). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Studying and understanding of fundamental advanced concepts in algorithms, data structures and complexity theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Will be handed out. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Introduction to Algorithms by T. H. Cormen, C. E. Leiserson, R. L. Rivest; Randomized Algorithms by R. Motwani und P. Raghavan; Computational Geometry - Algorithms and Applications by M. de Berg, M. van Kreveld, M. Overmars, O. Schwarzkopf. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-0210-00L | Compiler Design | O | 8 credits | 4V + 3U | Z. Su | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course uses compilers as examples to expose students to modern software development techniques. Tentative topics include: compiler organization; lexical analysis; top-down and bottom-up parsing; symbol tables; semantic analysis; code generation; local and global optimization; register allocation; automatic memory management. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Learn principles of compiler design; gain practical experience designing and implementing a medium-scale software system. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This course uses compilers as example to expose modern software development techniques. The course introduces the students to the fundamentals of compiler construction. Students will implement a simple yet complete compiler for an object-oriented programming language for a realistic target machine. Students will learn the use of appropriate tools. Throughout the course, students learn to apply their knowledge of theory (automata, grammars, stack machines, program transformation) and well-known programming techniques (module definitions, design patterns, frameworks, software reuse) in a software project. A tentative list of topics: compiler organization; lexical analysis; top-down and bottom-up parsing; symbol tables; semantic analysis; code generation; local and global optimization; register allocation; automatic memory management; optional advanced topics if/when time permits. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Aho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition) Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufmann Publishers, 1997 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Prior exposure to modern techniques for program construction, knowledge of at least one processor architecture at the assembly language level. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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252-0217-00L | Computer Systems ![]() | O | 8 credits | 4V + 2U + 1A | T. Roscoe, S. Shinde, R. Wattenhofer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course is about real computer systems, and the principles on which they are designed and built. We cover both modern OSes and the large-scale distributed systems that power today's online services. We illustrate the ideas with real-world examples, but emphasize common theoretical results, practical tradeoffs, and design principles that apply across many different scales and technologies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The objective of the course is for students to understand the theoretical principles, practical considerations, performance tradeoffs, and engineering techniques on which the software underpinning almost all modern computer systems is based, ranging from single embedded systems-on-chip in mobile phones to large-scale geo-replicated groups of datacenters. By the end of the course, students should be able to reason about highly complex, real, operational software systems, applying concepts such as hierarchy, modularity, consistency, durability, availability, fault-tolerance, and replication. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This course subsumes the topics of both "operating systems" and "distributed systems" into a single coherent picture (reflecting the reality that these disciplines are highly converged). The focus is system software: the foundations of modern computer systems from mobile phones to the large-scale geo-replicated data centers on which Internet companies like Amazon, Facebook, Google, and Microsoft are based. We will cover a range of topics, such as: scheduling, network protocol stacks, multiplexing and demultiplexing, operating system structure, inter-process communication, memory managment, file systems, naming, dataflow, data storage, persistence, and durability, computer systems performance, remove procedure call, consensus and agreement, fault tolerance, physical and logical clocks, virtualization, and blockchains. The format of the course is a set of about 25 topics, each covered in a lecture. A script will be published online ahead of each lecture, and the latter will consist of an interactive elaboration of the material in the script. There is no book for the course, but we will refer to books and research papers throughout to provide additional background and explanation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | We will assume knowlege of the "Systems Programming" and "Computer Networks" courses (or equivalent), and their prerequisites, and build upon them. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
![]() 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-0317-00L | Visualization, Simulation and Interaction - Virtual Reality II | W | 4 credits | 3G | A. Kunz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This lecture provides deeper knowledge on the possible applications of virtual reality, its basic technolgy, and future research fields. The goal is to provide a strong knowledge on Virtual Reality for a possible future use in business processes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Virtual Reality can not only be used for the visualization of 3D objects, but also offers a wide application field for small and medium enterprises (SME). This could be for instance an enabling technolgy for net-based collaboration, the transmission of images and other data, the interaction of the human user with the digital environment, or the use of augmented reality systems. The goal of the lecture is to provide a deeper knowledge of today's VR environments that are used in business processes. The technical background, the algorithms, and the applied methods are explained more in detail. Finally, future tasks of VR will be discussed and an outlook on ongoing international research is given. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Introduction into Virtual Reality; basisc of augmented reality; interaction with digital data, tangible user interfaces (TUI); basics of simulation; compression procedures of image-, audio-, and video signals; new materials for force feedback devices; intorduction into data security; cryptography; definition of free-form surfaces; digital factory; new research fields of virtual reality | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | The handout is available in German and English. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: "Visualization, Simulation and Interaction - Virtual Reality I" is recommended, but not mandatory. Didactical concept: The course consists of lectures and exercises. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies![]() |
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227-0124-00L | Embedded Systems ![]() ![]() | W | 6 credits | 4G | M. Magno, L. Thiele | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | An embedded system is a combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. The course covers theoretical and practical aspects of embedded system design and includes a series of lab sessions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Understanding specific requirements and problems arising in embedded system applications. Understanding architectures and components, their hardware-software interfaces, the memory architecture, communication between components, embedded operating systems, real-time scheduling theory, shared resources, low-power and low-energy design as well as hardware architecture synthesis. Using the formal models and methods in embedded system design in practical applications using the programming language C, the operating system ThreadX, a commercial embedded system platform, and the associated design environment. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | An embedded system is a combination of computer hardware and software, either fixed in capability or programmable, that is designed for a specific function or for specific functions within a larger system. For example, they are part of industrial machines, agricultural and process industry devices, automobiles, medical equipment, cameras, household appliances, airplanes, sensor networks, internet-of-things, as well as mobile devices. The focus of this lecture is on the design of embedded systems using formal models and methods as well as computer-based synthesis methods. Besides the theoretical lecture, the course is complemented by laboratory sessions where students learn to program an embedded system platform including sensors using C, to base their design on the embedded operating system ThreadX, and to edit/debug via an integrated development environment. Specifically, the following topics will be covered in the course: Embedded system architectures and components, hardware-software interfaces and memory architecture, software design methodology, communication, embedded operating systems, real-time scheduling, shared resources, low-power and low-energy design, and hardware architecture synthesis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Lecture material, publications, exercise sheets, and laboratory documentation will be available on the course's Moodle page. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Yifeng Zhu: Embedded Systems with Arm Cortex-M Microcontrollers in Assembly Language and C - Fourth Edition, E-Man Press LLC, ISBN: 978-0982692677, 2023 Giorgio C. Butazzo: Hard Real-Time Computing Systems. Predictable Scheduling Algorithms and Applications, Springer, ISBN 978-1-4614-3019-3, 2011 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Basic knowledge in computer architectures and programming. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Competencies![]() |
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227-0627-00L | Applied Computer Architecture | W | 6 credits | 4G | A. Gunzinger | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This lecture gives an overview of the requirements and the architecture of parallel computer systems, performance, reliability and costs. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Understand the function, the design and the performance modeling of parallel computer systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The lecture "Applied Computer Architecture" gives technical and corporate insights in innovative Computer Systems/Architectures (CPU, GPU, FPGA, dedicated processors) and their real implementations and applications. Often the designs have to deal with technical limits. Which computer architecture allows the control of the over 1000 magnets at the Swiss Light Source (SLS)? Which architecture is behind the alarm center of the Swiss Railway (SBB)? Which computer architectures are applied for driver assistance systems? Which computer architecture is hidden behind a professional digital audio mixing desk? How can data streams of about 30 TB/s, produced by a protone accelerator, be processed in real time? Can the weather forecast also be processed with GPUs? How can a good computer architecture be found? Which are the driving factors in succesful computer architecture design? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Script and exercices sheets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Basics of computer architecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-1037-00L | Introduction to Neuroinformatics ![]() | W | 6 credits | 2V + 1U + 1A | V. Mante, M. Cook, B. Grewe, G. Indiveri, D. Kiper, W. von der Behrens | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course provides an introduction to the functional properties of neurons. Particularly the description of membrane electrical properties (action potentials, channels), neuronal anatomy, synaptic structures, and neuronal networks. Simple models of computation, learning, and behavior will be explained. Some artificial systems (robot, chip) are presented. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Understanding computation by neurons and neuronal circuits is one of the great challenges of science. Many different disciplines can contribute their tools and concepts to solving mysteries of neural computation. The goal of this introductory course is to introduce the monocultures of physics, maths, computer science, engineering, biology, psychology, and even philosophy and history, to discover the enchantments and challenges that we all face in taking on this major 21st century problem and how each discipline can contribute to discovering solutions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This course considers the structure and function of biological neural networks at different levels. The function of neural networks lies fundamentally in their wiring and in the electro-chemical properties of nerve cell membranes. Thus, the biological structure of the nerve cell needs to be understood if biologically-realistic models are to be constructed. These simpler models are used to estimate the electrical current flow through dendritic cables and explore how a more complex geometry of neurons influences this current flow. The active properties of nerves are studied to understand both sensory transduction and the generation and transmission of nerve impulses along axons. The concept of local neuronal circuits arises in the context of the rules governing the formation of nerve connections and topographic projections within the nervous system. Communication between neurons in the network can be thought of as information flow across synapses, which can be modified by experience. We need an understanding of the action of inhibitory and excitatory neurotransmitters and neuromodulators, so that the dynamics and logic of synapses can be interpreted. Finally, simple neural architectures of feedforward and recurrent networks are discussed in the context of co-ordination, control, and integration of sensory and motor information. Connections to computer science and artificial intelligence are discussed, but the main focus of the course is on establishing the biological basis of computations in neurons. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-0293-00L | Wireless Networking and Mobile Computing ![]() | W | 4 credits | 2V + 1U | S. Mangold | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course gives an overview about wireless standards and summarizes the state of art for Wi-Fi 802.11, Cellular 5G, and Internet-of-Things, contact tracing with Bluetooth, audio communication, visible light communications, medical technology. The course combines lectures with a set of assignments in which students are asked to work with a JAVA simulation tool, and Arduino boards. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The objective of the course is to learn about the general principles of wireless communications, including physics, frequency spectrum regulation, and standards. Further, the most up-to-date standards and protocols used for wireless LAN IEEE 802.11, Wi-Fi, Internet-of-Things, sensor networks, cellular networks, visible light communication, and cognitive radios, are analyzed and evaluated. Students develop their own add-on mobile computing algorithms to improve the behavior of the systems, using a Java-based event-driven simulator. We also hand out embedded systems that can be used for experiments for optical communication. Throughout the course, insights from telecommunications, toy industry, and medical technology industry are shared. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Wireless Communication, Wi-Fi, Contact Tracing, Bluetooth, Internet-of-Things, 5G, Standards, Regulation, Algorithms, Radio Spectrum, Cognitive Radio, Mesh Networks, Optical Communication, Visible Light Communication. We will address contact tracing, radio link budget, location distance measurements, and Bluetooth in more depth. MedTech basics are also provided. Chapters: 1 Introduction 2 Wireless Communication Basics 3 IEEE 802.11 Wireless LAN (Wi-Fi) 4 IEEE 802.15 Wireless PAN (ZigBee & Bluetooth) 5 Mobile Computing Algorithm Basics: Control and Game Theory 6 Visible Light Communication 7 Audio Communication 8 Cellular Networking Basics (LTE, 5G, Internet-of-Things) 9 Mobile Computing for Automated Medicine Delivery 10 Cognitive Radio, Delay Tolerant Networking, Radio Spectrum Sharing | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | The course material will be made available by the lecturer. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | (1) The course webpage (look for Stefan Mangold's site) (2) The Java 802 protocol emulator "JEmula802" from https://bitbucket.org/lfield/jemula802 (3) WALKE, B. AND MANGOLD, S. AND BERLEMANN, L. (2006) IEEE 802 Wireless Systems Protocols, Multi-Hop Mesh/Relaying, Performance and Spectrum Coexistence. New York U.S.A.: John Wiley & Sons. Nov 2006. (4) BERLEMANN, L. AND MANGOLD, S. (2009) Cognitive Radio for Dynamic Spectrum Access . New York U.S.A.: John Wiley & Sons. Jan 2009. (5) MANGOLD, S. ET.AL. (2003) Analysis of IEEE 802.11e for QoS Support in Wireless LANs. IEEE Wireless Communications, vol 10 (6), 40-50. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Students should have interest in wireless communication, and should be familiar with Java programming. Experience with GNU Octave or Matlab will help too (not required). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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252-2810-00L | Fundamentals of Web Engineering ![]() | W | 5 credits | 2V + 2U | M. El-Assady, D. Sichau | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Contemporary web development utilizes a technology stack that spans from back-ends to front-ends, and includes virtual server environments, document databases, back-end and front-end programming, and UI/UX design. The depth of this stack fosters separation of concern and reuse, but also amounts to a steep learning curve. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | This course introduces both theoretical and applied aspects of web engineering. It covers: - DOM, CSS, Typescript - Fronted and backend frameworks - Client-server communication - Interaction design, visualization and narrative storytelling - Security for in the context of web engineering - Desktop applications using web development techniques | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The course has two main objectives: - Obtain an end-to-end (both, theoretical and practical) understanding of the foundations of web engineering. - Be able to apply these techniques in practice. While the lecture will provide the theoretical foundations for the various aspects of web engineering, the students will apply those techniques in project work that will span over the whole semester - involving different aspects of web engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | The lecture slides are available for download on the course page. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | To contact us please us the following email: web-foundations@ethz.ch Students should be familiar with the basics of a programming language (C, C++, Python, Java, Javascript, Typescript). The course will not teach basics of programming. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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402-0209-00L | Quantum Physics for Non-Physicists | W | 6 credits | 3V + 2U | P. Kammerlander | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This is an introduction to the physics of quantum mechanics following an information-theoretical approach. We start from the basic postulates, study the behaviour of quantum systems from a single spin to entangled particles in space, and connect the learnings to groundbreaking experiments from the past and the present. This course is well-suited for students with little background in physics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | This course teaches the basics of quantum physics, and complements courses in quantum computation and information theory. Students are equipped with tools to tackle complex quantum mechanical problems and foundational questions. The course covers approximately the same content as QM1, but from an information-driven perspective. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Quantum formalism, from qubits to particles in space; Time and dynamics for quantum systems; Problems in 1D; Uncertainty and open systems; Spin; Problems in 3D; Non-locality and foundational aspects of quantum theory | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Lecture notes will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Quantum Processes Systems, and Information, by Benjamin Schumacher and Michael Westmoreland, available at Link | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | This course is aimed at non-physicists, and in particular at students with a background in computer science, mathematics or engineering. Basic linear algebra and calculus knowledge is required (equivalent to first-year courses). Physics knowledge is not required. Physicists and students from a different background than outlined above are welcome at their own risk. Note that while we follow an information-theoretical approach, this is not a course on quantum information theory or quantum computing. It therefore complements those courses offered at ETH Zurich. This course can be taken in parallel to Quantum Information Processing I & II. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-2211-00L | Seminar in Computer Architecture ![]() ![]() The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | S. Sadrosadati, J. Gómez Luna, O. Mutlu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | In this seminar course, we will cover fundamental and cutting-edge research papers in computer architecture. The course will consist of multiple components that are aimed at improving students' technical skills in computer architecture, critical thinking and analysis on computer architecture concepts, as well as technical presentation of concepts and papers in both spoken and written forms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The main objective is to learn how to rigorously analyze and present papers and ideas on computer architecture. We will have rigorous presentation and discussion of selected papers during lectures and a written report delivered by each student at the end of the semester. This course is for those interested in computer architecture. Registered students are expected to attend every lecture, participate in the discussion, and create a synthesis report at the end of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Topics will center around computer architecture. We will, for example, discuss papers on hardware security; new execution paradigms like processing in memory; architectural acceleration mechanisms for key applications like machine learning, graph processing and bioinformatics; memory systems; interconnects; various fundamental and emerging paradigms in computer architecture; hardware/software co-design and cooperation; fault tolerance; energy efficiency; heterogeneous and parallel systems; technology scaling; new execution models, etc. See https://safari.ethz.ch/architecture_seminar for past examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | All the materials will be posted on the course website: https://safari.ethz.ch/architecture_seminar/ Links to past course materials, including the synthesis report assignment, can be found in this page: https://safari.ethz.ch/architecture_seminar | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Key papers and articles, on both fundamentals and cutting-edge topics in computer architecture will be provided and discussed. These will be posted on the course website. See https://safari.ethz.ch/architecture_seminar for past examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Digital Design and Computer Architecture OR Digital Circuits / Computer Engineering Students should (1) have done very well in Digital Design and Computer Architecture , Digital Circuits or a similar course and (2) show a genuine interest in Computer Architecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-2300-00L | Neural Networks and Computational Complexity ![]() The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | R. Cotterell | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Dependency parsing is a fundamental task in natural language processing. This seminar explores a variety of algorithms for efficient dependency parsing and their derivatioin in a unified algebraic framework. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The core ideas behind the mathematics of dependency parsing are explored. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Dependency Structures and Lexicalized Grammars: An Algebraic Approach | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-2600-05L | Software Engineering Seminar ![]() ![]() The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | Z. Su, M. Vechev | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course is an introduction to research in software engineering, based on reading and presenting high quality research papers in the field. The instructor may choose a variety of topics or one topic that is explored through several papers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The main goals of this seminar are 1) learning how to read and understand a recent research paper in computer science; and 2) learning how to present a technical topic in computer science to an audience of peers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The technical content of this course falls into the general area of software engineering but will vary from semester to semester. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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252-3400-00L | Seminar on Machine Learning Systems ![]() ![]() The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | A. Klimovic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This seminar covers core concepts and ideas in the general area of machine learning systems, ranging from distributed and federated learning systems, DevOps systems for ML, life cycle and data management systems for ML, etc. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The seminar covers core concepts and ideas in the general area of machine learning systems, ranging from distributed and federated learning systems, DevOps systems for ML, life cycle and data management systems for MLs, etc. The focus will be to cover fundamental ideas on ML systems, with an emphasis on software systems and platforms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The seminar will consist of student presentations based on a list of papers that will be provided at the beginning of the course. Presentations will be done in teams. Presentations will be arranged in slots of 30 minutes talk plus 15 minutes questions. Grades will be assigned based on quality of the presentation, coverage of the topic including material not in the original papers, participation during the seminar, and ability to understand, present, and criticize the underlying technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-3811-00L | Case Studies from Practice Seminar ![]() The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 4 credits | 2S | M. Brandis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Participants will learn how to analyze and solve IT problems in practice in a systematic way, present findings to decision bodies, and defend their conclusions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Participants understand the different viewpoints for IT-decisions in practice, including technical and business aspects, can effectively analyze IT questions from the different viewpoints and facilitate decision making. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Participants learn how to systematically approach an IT problem in practice. They work in groups of three to solve a case from a participating company in depth, studying provided materials, searching for additional information, analyzing all in depth, interviewing members from the company or discussing findings with them to obtain further insights, and presenting and defending their conclusion to company representatives, the lecturer, and all other participants of the seminar. Participants also learn how to challenge presentations from other teams, and obtain an overview of learnings from the cases other teams worked on. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Methodologies to analyze the cases and create final presentations. Short overview of each case. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Successful completion of Lecture "Information Technology in Practice". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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252-4811-00L | Machine Learning Seminar ![]() The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | V. Boeva, E. Krymova, L. Salamanca Miño | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Seminal and recent papers in machine learning are presented and discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The seminar familiarizes students with advanced and recent ideas in machine learning. Original articles have to be presented, contexctualized, and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The seminar will cover a number of recent papers which have emerged as important contributions in the machine learning research community. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | The papers will be presented and allocated in the first session of the seminar. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Basic knowledge of machine learning as taught in undergraduate courses such as "252-0220-00 Introduction to Machine Learning" are required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-5707-00L | Seminar on Media Innovation ![]() ![]() The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar. | W | 2 credits | 2S | S. Kalloori Saikishore, F. Zünd, M. El Helou | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This seminar introduces students to research and innovation in the area of media technology. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The objectives of this seminar are twofold: (1) learning about recent developments in the area of media technology at the intersection of computer vision, computer graphics, natural language processing, and machine learning and (2) to improve presentation and critical analysis skills. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The media industry is undergoing a fundamental transformation caused by digitalization. Media consumption is shifting away from traditional media such as TV or newspaper towards mobile and delayed consumption. The boundaries between media producers and consumers are getting blurred, and personalized content is increasingly important. Machine learning and AI are crucial tools to help to create better content, understand the consumers’ preferences and surface the essential stories in times of information overload. This seminar introduces students to the latest research in the field of media technology and innovation. It is an exciting field laying at the intersection of computer vision, computer graphics, natural language processing, and machine learning. The seminar will cover a broad spectrum of topics considering not only the technical innovations but also the possibilities these technologies provide to professionals in the media industry and consumers of media. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
101-0250-00L | Solving Partial Differential Equations in Parallel on GPUs ![]() | W | 4 credits | 3G | L. Räss, S. Omlin, I. Utkin, M. Werder | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course aims to cover state-of-the-art methods in modern parallel computing on Graphics Processing Unit (GPU), supercomputing and code development with applications to natural sciences and engineering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | When quantitative assessment of physical processes governing natural and engineered systems relies on numerically solving differential equations, fast and accurate solutions require performant algorithms leveraging parallel hardware. The goal of this course is to offer a practical approach to solve systems of differential equations in parallel on GPUs using the Julia language. Julia combines high-level language conciseness to low-level language performance which enables efficient code development. The course will be taught in a hands-on fashion, putting emphasis on you writing code and completing exercises; lecturing will be kept at a minimum. In a final project you will solve a solid mechanics or fluid dynamics problem of your interest, such as the shallow water equation, the shallow ice equation, acoustic wave propagation, nonlinear diffusion, viscous flow, elastic deformation, viscous or elastic poromechanics, frictional heating, and more. Your Julia GPU application will be hosted on a git-platform and implement modern software development practices. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Part 1 - Discovering a modern parallel computing ecosystem - Learn the basics of the Julia language; - Learn how to solve diffusion, wave propagation and advection processes; - Implement efficient iterative algorithms; - Get started with software development tools: git, version control. Part 2 - Developing your own parallel algorithms on GPUs - Implement wave propagation and porous convection; - Apply spatial and temporal discretisation (finite-differences, various time-stepper); - Understand the practical challenges of parallel computing: GPUs, multi-core CPUs; - Learn about main simulation performance limiters; - Implement software development tooling: unit tests, continuous integration (CI). Part 3 - Multi-GPU computing projects - Understand the practical challenges of distributed parallel computing on multi-GPUs; - Implement shared (on CPU and GPU) and distributed memory parallelisation (multi-GPUs/CPUs); - Automatise the software tooling using remote runners. Final projects - Apply your new skills in a final project; - Implement advanced physical processes (solid and fluid dynamic - elastic and viscous solutions). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Digital lecture notes, interactive Julia notebooks, online material. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Links to relevant literature will be provided during classes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Completed BSc studies. Interest in and basic knowledge of numerics, applied mathematics, and physics/engineering sciences. Basic programming skills (in e.g. Matlab, Python, Julia); advanced programming skills are a plus. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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151-0575-01L | Signals and Systems ![]() | W | 4 credits | 2V + 2U | A. Carron | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Signals 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Master the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Discrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Lecture notes available on course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Control Systems I is helpful but not required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
151-0591-00L | Control Systems I ![]() | W | 4 credits | 2V + 2U | E. Frazzoli | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Analysis and synthesis for linear time-invariant control systems with one input and one output signal (SISO). State-space models, time response, stability conditions. Transfer functions and frequency response. Stability analysis under feedback: Root Locus, Bode plots, Nyquist condition. Feedback control synthesis: time- and frequency-domain specifications, PID lead/lag compensation, loop shaping. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The course addresses dynamic control systems, i.e., systems that (i) evolve over time, and (ii) have control inputs and measured outputs. The main objective is to learn how to design the control inputs in such a way that the measured outputs have some desirable properties. For example, for an advanced driver assistance system, how to control acceleration so that the speed remains constant, and how to control the steering angle so that the car remains in the center of the lane. In order to pursue this objective, the course is organized into three main parts: 1) Modeling: learn how to represent a dynamic control system in such a way that it can be treated effectively using comutational and mathematical tools. This will include learning how to use computer tools like Matlab to simulate dynamic control systems. 2) Analysis: understand the basic characteristics of a system, such as its (internal and external) stability, performance, and robustness, and how the input affects the output. We will also learn to analyze systems obtained as interconnections (e.g., feedback) of two or more other systems. In particular, we will focus on tools that allow to understand how a system will behave under feedback control (i.e., closed-loop behavior), based only on its open-loop behavior. 3) Synthesis: the last part of the course will concentrate on how to design feedback control laws, in order to change the behavior of the system in a desirable way. In this course, we will concentrate on systems that can be modeled by Ordinary Differential Equations (ODEs), and that satisfy certain other technical conditions, such as linearity and time-invariance. In addition, we will focus on systems with a Single Input and a Single Output (SISO). This will allow us to use "classical control" tools that are very powerful and easy to use (i.e., mostly graphical), and which are really laying the foundation of any followup work on more challenging control problems. In addition to paper-and-pencil techniques, we will leverage modern computational tools for control design, such as Matlab. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Lecture slides and additional material will be posted online. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | There is no required textbook. A nice introductory book on feedback control, available online for free, is : Feedback Systems: An Introduction for Scientists and Engineers Karl J. Astrom and Richard M. Murray http://www.cds.caltech.edu/~murray/amwiki/index.php/First_Edition | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Basic knowledge of (complex) analysis and linear algebra. Familiarity with Matlab is recommended. For students in the bachelor's degree programme in mechanical engineering: Precondition for this course unit are passed first year examination blocks A and B. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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151-0709-00L | Stochastic Methods for Engineers and Natural Scientists | W | 4 credits | 4G | D. W. Meyer-Massetti | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course provides an introduction into stochastic methods that are applicable for example for the description and modeling of turbulent and subsurface flows. Moreover, mathematical techniques are presented that are used to quantify uncertainty in various engineering applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | By the end of the course you should be able to mathematically describe random quantities and their effect on physical systems. Moreover, you should be able to develop basic stochastic models of such systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | - Probability theory, single and multiple random variables, mappings of random variables - Estimation of statistical moments and probability densities based on data - Stochastic differential equations, Ito calculus, PDF evolution equations - Monte Carlo integration with importance and stratified sampling - Markov-chain Monte Carlo sampling - Control-variate and multi-level Monte Carlo estimation - Statistical tests for means and goodness-of-fit All topics are illustrated with engineering applications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Detailed lecture notes will be provided. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Some textbooks related to the material covered in the course: Stochastic Methods: A Handbook for the Natural and Social Sciences, Crispin Gardiner, Springer, 2010 The Fokker-Planck Equation: Methods of Solutions and Applications, Hannes Risken, Springer, 1996 Turbulent Flows, S.B. Pope, Cambridge University Press, 2000 Spectral Methods for Uncertainty Quantification, O.P. Le Maitre and O.M. Knio, Springer, 2010 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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227-0075-00L | Electrical Engineering I | W | 4 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; | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | For students in the bachelor's degree programme in mechanical engineering: Precondition for this course unit are passed first year examination blocks A and B. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-0076-00L | Electrical Engineering II Does not take place this semester. | W | 4 credits | 2V + 2U | C. Studer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Sinusoidal signals and systems in the time and frequency domain, principle of operation and design of basic analog and digital circuits as well as analog-digital conversion. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | see above | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Beschreibung von sinusförmigen Signalen und Systemen im Zeit- und Frequenzbereich, Funktion grundlegender analoger und digitaler Schaltungen sowie von Analog-Digital-Wandlern. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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227-0116-00L | VLSI 1: HDL Based Design for FPGAs ![]() | W | 6 credits | 5G | F. K. Gürkaynak, L. Benini | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This first course in a series that extends over three consecutive terms is concerned with tailoring algorithms and with devising high performance hardware architectures for their implementation as ASIC or with FPGAs. The focus is on front end design using HDLs and automatic synthesis for producing industrial-quality circuits. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Understand Very-Large-Scale Integrated Circuits (VLSI chips), Application-Specific Integrated Circuits (ASIC), and Field-Programmable Gate-Arrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in front-end design from architectural conception to gate-level netlists. How to model digital circuits with SystemVerilog. How to ensure they behave as expected with the aid of simulation, testbenches, and assertions. How to take advantage of automatic synthesis tools to produce industrial-quality VLSI and FPGA circuits. Gain practical experience with the hardware description language SystemVerilog and with industrial Electronic Design Automation (EDA) tools. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This course is concerned with system-level issues of VLSI design and FPGA implementations. Topics include: - Overview on design methodologies and fabrication depths. - Levels of abstraction for circuit modeling. - Organization and configuration of commercial field-programmable components. - FPGA design flows. - Dedicated and general purpose architectures compared. - How to obtain an architecture for a given processing algorithm. - Meeting throughput, area, and power goals by way of architectural transformations. - Hardware Description Languages (HDL) and the underlying concepts. - SystemVerilog - Register Transfer Level (RTL) synthesis and its limitations. - Building blocks of digital VLSI circuits. - Functional verification techniques and their limitations. - Modular and largely reusable testbenches. - Assertion-based verification. - Synchronous versus asynchronous circuits. - The case for synchronous circuits. - Periodic events and the Anceau diagram. - Case studies, ASICs compared to microprocessors, DSPs, and FPGAs. During the exercises, students learn how to model FPGAs with SystemVerilog. They write testbenches for simulation purposes and synthesize gate-level netlists for FPGAs. Commercial EDA software by leading vendors is being used throughout. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Textbook and all further documents in English. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Basics of digital circuits. Examination: In written form following the course semester (spring term). Problems are given in English, answers will be accepted in either English oder German. Further details: https://iis-students.ee.ethz.ch/lectures/vlsi-i/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-0731-00L | Power Market I - Portfolio and Risk Management | W | 6 credits | 4G | D. Reichelt, G. A. Koeppel | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Portfolio and risk management in the electrical power business, Pan-European power market and trading, futures and forward contracts, hedging, options and derivatives, performance indicators for the risk management, modelling of physical assets, cross-border trading, ancillary services, balancing power market, Swiss market model. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Knowlege on the worldwide liberalisation of electricity markets, pan-european power trading and the role of power exchanges. Understand financial products (derivatives) based on power. Management of a portfolio containing physical production, contracts and derivatives. Evaluate trading and hedging strategies. Apply methods and tools of risk management. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | 1. Pan-European power market and trading 1.1. Power trading 1.2. Development of the European power markets 1.3. Energy economics 1.4. Spot and OTC trading 1.5. European energy exchange EEX 2. Market model 2.1. Market place and organisation 2.2. Balance groups / balancing energy 2.3. Ancillary services 2.4. Market for ancillary services 2.5. Cross-border trading 2.6. Capacity auctions 3. Portfolio and Risk management 3.1. Portfolio management 1 (introduction) 3.2. Forward and futures contracts 3.3. Risk management 1 (m2m, VaR, hpfc, volatility, cVaR) 3.4. Risk management 2 (PaR) 3.5. Contract valuation (HPFC) 3.6. Portfolio management 2 2.8. Risk Management 3 (enterprise wide) 4. Energy & Finance I 4.1. Options 1 – basics 4.2. Options 2 – hedging with options 4.3. Introduction to derivatives (swaps, cap, floor, collar) 4.4. Financial modelling of physical assets 4.5. Trading and hydro power 4.6. Incentive regulation | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Handouts of the lecture | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | 1 excursion per semester, 2 case studies, guest speakers for specific topics. Course Moodle: https://moodle-app2.let.ethz.ch/enrol/index.php?id=11636 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
227-2037-00L | Physical Modelling and Simulation | W | 6 credits | 4G | J. Smajic | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This module consists of (a) an introduction to fundamental equations of electromagnetics, mechanics and heat transfer, (b) a detailed overview of numerical methods for field simulations, and (c) practical examples solved in form of small projects. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Basic knowledge of the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. Knowledge of the main concepts of numerical methods for physical modelling and simulation. Ability (a) to develop own simple field simulation programs, (b) to select an appropriate field solver for a given problem, (c) to perform field simulations, (d) to evaluate the obtained results, and (e) to interactively improve the models until sufficiently accurate results are obtained. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The module begins with an introduction to the fundamental equations and effects of electromagnetics, mechanics, and heat transfer. After the introduction follows a detailed overview of the available numerical methods for solving electromagnetic, thermal and mechanical boundary value problems. This part of the course contains a general introduction into numerical methods, differential and integral forms, linear equation systems, Finite Difference Method (FDM), Boundary Element Method (BEM), Method of Moments (MoM), Multiple Multipole Program (MMP) and Finite Element Method (FEM). The theoretical part of the course finishes with a presentation of multiphysics simulations through several practical examples of HF-engineering such as coupled electromagnetic-mechanical and electromagnetic-thermal analysis of MEMS. In the second part of the course the students will work in small groups on practical simulation problems. For solving practical problems the students can develop and use own simulation programs or chose an appropriate commercial field solver for their specific problem. This practical simulation work of the students is supervised by the lecturers. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
351-0778-00L | Discovering Management Entry level course in management for BSc, MSc and PHD students at all levels not belonging to D-MTEC. This course can be complemented with Discovering Management (Excercises) 351-0778-01. | W | 3 credits | 3G | B. Clarysse, S. Brusoni, F. Da Conceição Barata, V. Hoffmann, T. Netland, P. Tinguely, L. P. T. Vandeweghe | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Discovering Management offers an introduction to the field of business management and entrepreneurship for engineers and natural scientists. By taking this course, students will enhance their understanding of management principles and the tasks that entrepreneurs and managers deal with. The course consists of theory and practice sessions, presented by a set of area specialists at D-MTEC. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The general objective of Discovering Management is to introduce students into the field of business management and entrepreneurship. In particular, the aims of the course are to: (1) broaden understanding of management principles and frameworks (2) advance insights into the sources of corporate and entrepreneurial success (3) develop skills to apply this knowledge to real-life managerial problems The course will help students to successfully take on managerial and entrepreneurial responsibilities in their careers and / or appreciate the challenges that entrepreneurs and managers deal with. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The course consists of a set of theory and practice sessions, which will be taught on a weekly basis. The course will cover business management knowledge in corporate as well as entrepreneurial contexts. The course consists of three blocks of theory and practice sessions: Discovering Strategic Management, Discovering Innovation Management, and Discovering HR and Operations Management. Each block consists of two or three theory sessions, followed by one practice session where you will apply the theory to a case. The theory sessions will follow a "lecture-style" approach and be presented by an area specialist within D-MTEC. Practical examples and case studies will bring the theoretical content to life. The practice sessions will introduce you to some real-life examples of managerial or entrepreneurial challenges. During the practice sessions, we will discuss these challenges in depth and guide your thinking through team coaching. Through small group work, you will develop analyses of each of the cases. The theory sessions will be assessed via a multiple choice exam. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | All course materials (readings, slides, videos, and worksheets) will be made available to inscribed course participants through Moodle. These course materials will form the point of departure for the lectures, class discussions and team work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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351-0778-01L | Discovering Management (Pitch) Complementary exercises for the module Discovering Managment. Prerequisite: Participation and successful completion of the module Discovering Management (351-0778-00L) is mandatory. | W | 1 credit | 1U | B. Clarysse, L. P. T. Vandeweghe | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course is offered complementary to the basis course 351-0778-00L, "Discovering Management". The course offers an additional exercise. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The general objective of Discovering Management (Exercises) is to complement the course "Discovering Management" with one larger additional exercise. Discovering Management (Exercises) thus focuses on developing the skills and competences to apply management theory to a real-life exercise from practice. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The exercise consists of delivering and submitting a "pitch" with a clear recommendation for one of the selected cases amongst those seen in Discovering Management, using your insights from Discovering Management, and an extra session on pitching. Students have the option to either do this alone or in a group of two students. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | All course materials (readings, slides, videos, and worksheets) will be made available to inscribed course participants through Moodle. Students following this course should also be enrolled for course 351-0778-00L, "Discovering Management". | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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351-1109-00L | Introduction to Microeconomics GESS (Science in Perspective): This course is only for students enrolled in a Bachelor’s degree programme. Students enrolled in a Master’s degree programme may attend “Principles of Microeconomics” (LE 363-0503-00L) instead. Note for D-MAVT students: If you have already successfully completed “Principles of Microeconomics” (LE 363-0503-00L), then you will not be permitted to attend it again. | W | 3 credits | 2G | M. Wörter, M. Beck | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course introduces basic principles, problems and approaches of microeconomics. It describes economic decisions of households and firms, and their coordination through perfectly competitive markets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Students acquire a deeper understanding of basic microeconomic models. They acquire the ability to apply these models in the interpretation of real world economic contexts. Students acquire a reflective and contextual knowledge on how societies use scarce resources to produce goods and services and distribute them among themselves. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Market, budget constraint, preferences, utility function, utility maximisation, demand, technology, profit function, cost minimisation, cost functions, perfect competition, information and communication technologies | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Course material in e-learning environment https://moodle-app2.let.ethz.ch/auth/shibboleth/login.php | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Varian, Hal R. (2014), Intermediate Microeconomics, W.W. Norton | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | This course "Einführung in die Mikroökonomie“ (363-1109-00L) is intended for Bachelor students and LE 363-0503-00 "Principles of Microeconomics" for Master students. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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363-0541-00L | Economic Dynamics and Complexity | W | 3 credits | 3G | F. Schweitzer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | What causes economic business cycles? How are limited resources, competition, and cooperation reflected in growth dynamics? To answer such questions, we combine macroeconomic models and methods of nonlinear dynamics. We study the role of bifurcations and control parameters for dynamic stability. Feedback cycles and coupled dynamics are reasons for limited predictability, instability and chaos. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | successful participant of the course is able to: - understand the importance of different modeling approaches - formalize and solve one- and two-dimensional nonlinear models - identify critical conditions for stability and dynamic transitions - analyze macroeconomic models of business cycles, supply and demand - apply formal concepts to model economic growth and competition | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | System theory sees the economy as a complex adaptive system. What does this mean for economic modeling? We focus on two sources of complexity: (a) nonlinear dynamics, which is captured in this course, "Economic Dynamics and Complexity" and (b) collective interactions, which is captured in the course "Agent-Based Modeling of Economic Systems" (in Spring). Our approach to economic dynamics combines insights from different disciplines: macroeconomics studying business cycles and growth, system dynamics rooted general system theory and cybernetics, and nonlinear dynamics using applied mathematics. We start with a comparison of different modeling approaches, to highlight the problems and challenges of system modeling. The subsequent lectures then introduce different one- and two-dimensional nonlinear models with applications in economics, such as models of supply and demand, business cycles, growth and competition. Emphasis is on the formal analysis of these models using methods from applied mathematics and tools for solving coupled differential equations. Weekly self-study tasks are used to apply the concepts introduced in the lectures. We practice how to solve nonlinear models formally and numerically and how to interpret the results. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | The lecture slides are provided as handouts - including notes and literature sources - to registered students only. All material is to be found on the Moodle platform. More details during the first lecture. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Students should be familar with nonlinear differential equations and should have basic programming skills. All necessary details to solve nonlinear models will be provided in the course. The course will not build on mathematical proofs, optimization, statistics, efficient numerical computation and other specialized skills. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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363-1082-00L | Enabling Entrepreneurship: From Science to Startup ![]() Students should provide a brief overview (unto 1 page) of their business ideas that they would like to commercialise through the course. If they do not have an idea, they are required to provide a motivation letter stating why they would like to do this elective. If you are unsure about the readiness of your idea or technology to be converted into a startup, please drop me a line to schedule a call or meeting to discuss. The total number of students will be limited to 50. The students should submit the necessary information until 7 September 2023 and apply to Robin De Cock: Robin.DeCock@uantwerpen.be. | W | 3 credits | 2V | R. De Cock | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This elective is relevant for students who have developed a technology and are keen to evaluate the steps in starting a startup. This is also relevant for students who would like to start a startup but do not have a technology, but are clear on a specific market and the impact they would like to create. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Students have technology competence or an idea that they would like to convert into a startup. They are now in the process of evaluating the steps necessary to do so. In summary: 1. Students want to become entrepreneurs. 2. The students can be from business or science & technology. 3. The course will enable the students to identify the relevance of their technology or idea from the market relevance perspective and thereby create a business case to take it to market. 4. The students will have exposure to investors and entrepreneurs (with a focus on ETH spin-offs) through the course, to gain insight to commercialise their idea. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The students would cover the following topics, as the build their idea into a business case: 1. Technology excellence: this assumes that the student has achieved a certain degree of competence in the area of technology that he or she expects to bring to the market 2. Market need and market relevance: The student would then be expected to identify the possible markets that may find the technology of relevance. Market relevance implies the process of identification of how relevant the market perceives the technology, and whether this can sustain over a longer period of time 3. IP and IP strategy: Intellectual property, whether in the form of a patent or a trade secret, implies the secret ingredient that enables the student to achieve certain results that competitors are unable to copy. This enables the student (and subsequently the startup) to hold on to the market that they create with customers 4. Team including future capabilities required: a startup requires multiple people with complementary capabilities. They also need to be motivated while at the same time protecting the interests of the startup 5. Financials: There is a need of funding to achieve milestones. This includes funding for salaries and running of the company 6. Investors and funding options: There are multiple funding options for a startup. They all come with different advantages and limitations. It's important for a startup to recognise its needs and find the investors that fit these needs and are best aligned with the vision of the founders 7. Preparation of business case: The students will finally prepare the business case that can help them to articulate the link of the technology with the market need and its willingness to pay 8. Legal overview, company forms and shareholders’ agreements (including pitfalls) The seminar includes talks from invited investors, entrepreneurs and legal experts regarding the importance of the various elements being covered in content, workshops and teamwork. There is a particular emphasis on market validation on each step of the journey, to ensure relevance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Since the course will revolve around the ideas of the students, the notes will be for the sole purpose of providing guidance to the students to help convert their technologies or ideas into business cases for the purpose of forming startups. Theoretical subject matter will be kept to a minimum and is not the focus of the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Book Sethi, A. "From Science to Startup" ISBN 978-3-319-30422-9 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | This course is relevant for those students who aspire to become entrepreneurs. Students applying for this course are requested to submit a 1 page business idea or, in case they don't have a business idea, a brief motivation letter stating why they would like to do this course. If you are unsure about the readiness of your idea or technology to be converted into a startup, please drop me a line to schedule a call or meeting to discuss. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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363-1163-00L | Developing Digital Biomarkers ![]() Particularly suitable for students with a technical background who are interested in healthcare. | W | 3 credits | 2V | F. Da Conceição Barata | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course gives an introduction to digital biomarkers and provides students with the foundations to develop their own digital biomarkers. More specifically, the course will cover fundamental topics such as designing observational studies, collecting, and exploring data generated by consumer-centric devices, and applying analytical methods to predict health-related outcomes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The widespread use of mobile technologies (e.g., wearable sensors, mobile applications, social media, and location-tracking technologies) has the potential to meet the health monitoring needs of the world's aging population and the ever-growing number of chronic patients. However, this premise is based on the application of Machine Learning algorithms that allow us to use this data in many different ways. In this course we will analyze systematic ways to collect data, review the most relevant methods and applications in healthcare, discuss the main challenges they present and apply the newly gained knowledge in practical assignments. The course has four core learning objectives. Students should: • understand the anatomy of digital biomarkers • understand the potential and applications of digital biomarkers • be able to critically reflect and assess existing digital biomarkers • be able to design and implement a digital biomarker | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The course will consist of four topic clusters that will allow the discussion of the most relevant digital biomarker applications in healthcare: 1) Digital Biomarkers: From biological to digital biomarkers. How are they motivated, defined and how can they be leveraged for monitoring? Prognostic vs. diagnostic vs. predictive biomarkers. Passive sensing vs. active sensing. Digital biomarker vs. Digital therapeutics. 2) Consumer-centric device data: Today, vast amount of physiological, environmental, and behavioral observations can be collected with consumer centric devices. To derive clinical meaningful information from this data is, however, difficult. We will analyze strategies for extracting knowledge from those measurements. 3) Methodology: In the last decade, neural networks (also known as “deep learning”) have helped push the boundaries of the state-of-the-art in a myriad of domains. They have also uncovered a number of different problems. We will discuss advantages and disadvantage as well as alternative methods for their application to digital biomarker data. 4) Applications: Digital biomarkers are still an emerging subfield, but given that longitudinal in digital biomarker data are arguably easy to acquire in large quantities, it is expected that many relevant Machine Learning applications will emerge in the near future. We will review and discuss current applications and challenges. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | [1] Sim, Ida. "Mobile devices and health." New England Journal of Medicine 381.10 (2019): 956-968. [2] Schatz, Bruce R. "Population measurement for health systems." NPJ Digital Medicine 1.1 (2018): 1-4. [3] Coravos, Andrea, Sean Khozin, and Kenneth D. Mandl. "Developing and adopting safe and effective digital biomarkers to improve patient outcomes." NPJ digital medicine 2.1 (2019): 1-5. [4] van den Brink, Willem, et al. "Digital resilience biomarkers for personalized health maintenance and disease prevention." Frontiers in Digital Health 2 (2021): 54. [5] Weiser, Mark. "The computer for the 21st century." ACM SIGMOBILE mobile computing and communications review 3.3 (1999): 3-11. [6] Kvedar, Joseph C., et al. "Digital medicine's march on chronic disease." Nature biotechnology 34.3 (2016): 239-246. [7] Meskó, Bertalan, and Marton Görög. "A short guide for medical professionals in the era of artificial intelligence." NPJ digital medicine 3.1 (2020): 1-8. [8] Fogel, Alexander L., and Joseph C. Kvedar. "Artificial intelligence powers digital medicine." NPJ digital medicine 1.1 (2018): 1-4. [9] Caruana, Rich, et al. "Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission." Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. 2015. [10] McCradden, Melissa D., et al. "Ethical limitations of algorithmic fairness solutions in health care machine learning." The Lancet Digital Health 2.5 (2020): e221-e223. [11] Gebru, Timnit, et al. "Datasheets for datasets." Communications of the ACM 64.12 (2021): 86-92. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Some programming experience in Python is required, and some experience in Machine Learning is highly recommended. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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376-1177-00L | Human Factors I | W | 3 credits | 2V | M. Menozzi Jäckli, R. Huang | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Strategies of human-system-interaction, individual needs, physical & mental abilities, and system properties are key factors affecting the quality and performance in interaction processes. In the lecture, factors are investigated by basic scientific approaches. Discussed topics are important for optimizing people's health, well-being, and satisfaction as well as the overall system performance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The goal of the lecture is to empower students in better understanding the applied theories, principles, and methods in various applications. Students are expected to learn about how to enable an efficient and qualitatively high standing interaction between human and the environment, considering costs, benefits, health, and safety as well. Thus, an ergonomic design and evaluation process of products, tasks, and environments may be promoted in different disciplines. The goal is achieved in addressing a broad variety of topics and embedding the discussion in macroscopic factors such as the behavior of consumers and objectives of economy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | - Physiological, physical, and cognitive factors in sensation, perception, and action - Body spaces and functional anthropometry, Digital Human Models - Experimental techniques in assessing human performance, well-being, and comfort - Usability engineering in system designs, product development, and innovation - Human information processing and biological cybernetics - Interaction among consumers, environments, behavior, and tasks | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | - Gavriel Salvendy, Handbook of Human Factors and Ergonomics, 4th edition (2012), is available on NEBIS as electronic version and for free to ETH students - Further textbooks are introduced in the lecture - Brouchures, checklists, key articles etc. are uploaded in ILIAS | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-0353-00L | Analysis 3 ![]() ![]() | W | 4 credits | 2V + 2U | M. Iacobelli | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | In this lecture we treat problems in applied analysis. The focus lies on the solution of quasilinear first order PDEs with the method of characteristics, and on the study of three fundamental types of partial differential equations of second order: the Laplace equation, the heat equation, and the wave equation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The aim of this class is to provide students with a general overview of first and second order PDEs, and teach them how to solve some of these equations using characteristics and/or separation of variables. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | 1.) General introduction to PDEs and their classification (linear, quasilinear, semilinear, nonlinear / elliptic, parabolic, hyperbolic) 2.) Quasilinear first order PDEs - Solution with the method of characteristics - COnservation laws 3.) Hyperbolic PDEs - wave equation - d'Alembert formula in (1+1)-dimensions - method of separation of variables 4.) Parabolic PDEs - heat equation - maximum principle - method of separation of variables 5.) Elliptic PDEs - Laplace equation - maximum principle - method of separation of variables - variational method | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Y. Pinchover, J. Rubinstein, "An Introduction to Partial Differential Equations", Cambridge University Press (12. Mai 2005) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Analysis I and II, Fourier series (Complex Analysis) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-0625-01L | Applied Analysis of Variance and Experimental Design | W | 5 credits | 2V + 1U | L. Meier | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Principles of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Participants will be able to plan and analyze efficient experiments in the fields of natural sciences. They will gain practical experience by using the software R. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Principles of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | G. Oehlert: A First Course in Design and Analysis of Experiments, W.H. Freeman and Company, New York, 2000. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | The exercises, but also the classes will be based on procedures from the freely available, open-source statistical software R, for which an introduction will be held. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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401-3913-01L | Mathematical Foundations for Finance ![]() | W | 4 credits | 3V + 2U | B. Acciaio | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | First introduction to main modelling ideas and mathematical tools from mathematical finance | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | This course gives a first introduction to the main modelling ideas and mathematical tools from mathematical finance. It mainly aims at non-mathematicians who need an introduction to the main tools from stochastics used in mathematical finance. However, mathematicians who want to learn some basic modelling ideas and concepts for quantitative finance (before continuing with a more advanced course) may also find this of interest.. The main emphasis will be on ideas, but important results will be given with (sometimes partial) proofs. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Topics to be covered include - financial market models in finite discrete time - absence of arbitrage and martingale measures - valuation and hedging in complete markets - basics about Brownian motion - stochastic integration - stochastic calculus: Itô's formula, Girsanov transformation, Itô's representation theorem - Black-Scholes formula | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | See information on course homepage | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Prerequisites: Results and facts from probability theory as in the book "Probability Essentials" by J. Jacod and P. Protter will be used freely. Especially participants without a direct mathematics background are strongly advised to familiarise themselves with those tools before (or very quickly during) the course. (A possible alternative to the above English textbook are the (German) lecture notes for the standard course "Wahrscheinlichkeitstheorie".) For those who are not sure about their background, we suggest to look at the exercises in Chapters 8, 9, 22-25, 28 of the Jacod/Protter book. If these pose problems, you will have a hard time during the course. So be prepared. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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401-4623-00L | Time Series Analysis Does not take place this semester. | W | 4 credits | 2G | N. Meinshausen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course offers an introduction into analyzing times series, that is observations which occur in time. The material will cover Stationary Models, ARMA processes, Spectral Analysis, Forecasting, Nonstationary Models, ARIMA Models and an introduction to GARCH models. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The goal of the course is to have a a good overview of the different types of time series and the approaches used in their statistical analysis. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | This course treats modeling and analysis of time series, that is random variables which change in time. As opposed to the i.i.d. framework, the main feature exibited by time series is the dependence between successive observations. The key topics which will be covered as: Stationarity Autocorrelation Trend estimation Elimination of seasonality Spectral analysis, spectral densities Forecasting ARMA, ARIMA, Introduction into GARCH models | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | The main reference for this course is the book "Introduction to Time Series and Forecasting", by P. J. Brockwell and R. A. Davis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Basic knowledge in probability and statistics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
401-7855-00L | Computational Astrophysics (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: AST245 Mind the enrolment deadlines at UZH: https://www.uzh.ch/cmsssl/en/studies/application/deadlines.html | W | 6 credits | 2V | L. M. Mayer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Learning objective | Acquire knowledge of main methodologies for computer-based models of astrophysical systems,the physical equations behind them, and train such knowledge with simple examples of computer programmes | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | 1. Integration of ODE, Hamiltonians and Symplectic integration techniques, time adaptivity, time reversibility 2. Large-N gravity calculation, collisionless N-body systems and their simulation 3. Fast Fourier Transform and spectral methods in general 4. Eulerian Hydrodynamics: Upwinding, Riemann solvers, Limiters 5. Lagrangian Hydrodynamics: The SPH method 6. Resolution and instabilities in Hydrodynamics 7. Initial Conditions: Cosmological Simulations and Astrophysical Disks 8. Physical Approximations and Methods for Radiative Transfer in Astrophysics | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Galactic Dynamics (Binney & Tremaine, Princeton University Press), Computer Simulation using Particles (Hockney & Eastwood CRC press), Targeted journal reviews on computational methods for astrophysical fluids (SPH, AMR, moving mesh) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Some knowledge of UNIX, scripting languages (see www.physik.uzh.ch/lectures/informatik/python/ as an example), some prior experience programming, knowledge of C, C++ beneficial | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
402-0809-00L | Introduction to Computational Physics | W | 8 credits | 2V + 2U | A. Adelmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course offers an introduction to computer simulation methods for physics problems and their implementation on PCs and super computers. The covered topics include classical equations of motion, partial differential equations (wave equation, diffusion equation, Maxwell's equations), Monte Carlo simulations, percolation, phase transitions, and N-Body problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Students learn to apply the following methods: Random number generators, Determination of percolation critical exponents, numerical solution of problems from classical mechanics and electrodynamics, canonical Monte-Carlo simulations to numerically analyze magnetic systems. Students also learn how to implement their own numerical frameworks in Julia and how to use existing libraries to solve physical problems. In addition, students learn to distinguish between different numerical methods to apply them to solve a given physical problem. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Introduction to computer simulation methods for physics problems. Models from classical mechanics, electrodynamics and statistical mechanics as well as some interdisciplinary applications are used to introduce modern programming methods for numerical simulations using Julia. Furthermore, an overview of existing software libraries for numerical simulations is presented. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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 | Lecture and exercise lessons in english, exams in German or in English | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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402-1701-00L | Physics I | W | 7 credits | 4V + 2U | K. Ensslin | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | This course gives a first introduction to Physics with an emphasis on classical mechanics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Acquire knowledge of the basic principles regarding the physics of classical mechanics. Skills in solving physics problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
636-0007-00L | Computational Systems Biology ![]() | W | 6 credits | 3V + 2U | J. Stelling | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | Study 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). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The aim of this course is to provide an introductory overview of mathematical and computational methods for the modeling, simulation and analysis of biological networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | Biology 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 notes | http://www.csb.ethz.ch/education/lectures.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | U. 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
651-4241-00L | Numerical Modelling I and II: Theory and Applications | W | 6 credits | 4G | T. Gerya | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | In this 13-week sequence, students learn how to write programs from scratch to solve partial differential equations that are useful for Earth science applications. Programming will be done in MATLAB and will use the finite-difference method and marker-in-cell technique. The course will emphasise a hands-on learning approach rather than extensive theory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | The goal of this course is for students to learn how to program numerical applications from scratch. By the end of the course, students should be able to write state-of-the-art MATLAB codes that solve systems of partial-differential equations relevant to Earth and Planetary Science applications using finite-difference method and marker-in-cell technique. Applications include Poisson equation, buoyancy driven variable viscosity flow, heat diffusion and advection, and state-of-the-art thermomechanical code programming. The emphasis will be on commonality, i.e., using a similar approach to solve different applications, and modularity, i.e., re-use of code in different programs. The course will emphasise a hands-on learning approach rather than extensive theory, and will begin with an introduction to programming in MATLAB. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | A provisional week-by-week schedule (subject to change) is as follows: Week 1: Introduction to the finite difference approximation to differential equations. Introduction to programming in Matlab. Solving of 1D Poisson equation. Week 2: Direct and iterative methods for obtaining numerical solutions. Solving of 2D Poisson equation with direct method. Solving of 2D Poisson equation with Gauss-Seidel and Jacobi iterative methods. Week 3: Solving momentum and continuity equations in case of constant viscosity with stream function/vorticity formulation. Weeks 4: Staggered grid for formulating momentum and continuity equations. Indexing of unknowns. Solving momentum and continuity equations in case of constant viscosity using pressure-velocity formulation with staggered grid. Weeks 5: Conservative finite differences for the momentum equation. "Free slip" and "no slip" boundary conditions. Solving momentum and continuity equations in case of variable viscosity using pressure-velocity formulation with staggered grid. Week 6: Advection in 1-D. Eulerian methods. Marker-in-cell method. Comparison of different advection methods and their accuracy. Week 7: Advection in 2-D with Marker-in-cell method. Combining flow calculation and advection for buoyancy driven flow. Week 8: "Free surface" boundary condition and "sticky air" approach. Free surface stabilization. Runge-Kutta schemes. Continuity-based velocity interpolation. Week 9: Solving 2D heat conservation equation in case of constant thermal conductivity with explicit and implicit approaches. Week 10: Solving 2D heat conservation equation in case of variable thermal conductivity with implicit approach. Temperature advection with markers. Creating thermomechanical code by combining mechanical solution for 2D buoyancy driven flow with heat diffusion and advection based on marker-in-cell approach. Week 11: Implementation of radioactive, adiabatic and shear heating to the thermomechanical code. Week 12: Programming of solution of coupled solid-fluid momentum and continuity equations for the case of melt percolation in a rising mantle plume. Week 13: Subgrid diffusion of temperature and its implementation. Implementation of temperature-, pressure- and strain rate-dependent viscosity, temperature- and pressure-dependent density and temperature-dependent thermal conductivity to the thermomechanical code. Final project description for slab breakoff modeling. GRADING will be based on weekly programming homeworks (50%) and a term project (50%) to develop an application of their choice to a more advanced level. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Taras Gerya, Introduction to Numerical Geodynamic Modelling. Second edition. Cambridge University Press 2019 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
701-0071-00L | Mathematics III: Systems Analysis | W | 4 credits | 2V + 1U | C. Brunner, R. Knutti, S. Schemm, H. Wernli | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The objective of the systems analysis course is to deepen and illustrate the mathematical concepts on the basis of a series of very concrete examples. Topics covered include: linear box models with one or several variables, non-linear box models with one or several variables, time-discrete models, and continuous models in time and space. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | Learning and applying of concepts (models) and quantitative methods to address concrete problems of environmental relevance. Understanding and applying the systems-analytic approach, i.e., Recognizing the core of the problem - simplification - quantitative approach - prediction. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | https://iac.ethz.ch/edu/courses/bachelor/vorbereitung/systemanalyse.html | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Lecture notes | Overhead slides will be made available through the course website. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Literature | Imboden, D.S. and S. Pfenninger (2013) Introduction to Systems Analysis: Mathematically Modeling Natural Systems. Berlin Heidelberg: Springer Verlag. http://link.springer.com/book/10.1007%2F978-3-642-30639-6 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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701-0901-02L | ETH Week 2023: Circular Realities ![]() All ETH Bachelor`s, Master`s and exchange students can take part in the ETH week. No prior knowledge is required | W | 1 credit | 3S | F. Rittiner, C. Bening-Bach, S. Brusoni, R. Knutti, A. Vaterlaus | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | ETH Week is a project-based course during which students define a problem and develop a solution related to the UN SDGs. Participants get the chance to attend talks and discussions with inspiring leaders and changemakers, go on field trips throughout the Zurich area, as well as meet and network with more than 60 experts. The 2023 edition explores the concept of a circular economy. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | - Domain-specific knowledge: Students have immersed knowledge about a certain complex, societal topic which will be selected every year. They understand the complex system context of the current topic, by comprehending its scientific, technical, political, social, ecological and economic perspectives. - Analytical skills: The ETH Week participants are able to structure complex problems systematically using selected methods. They are able to acquire further knowledge and critically analyse the knowledge in interdisciplinary groups and with experts and the help of team tutors. - Design skills: The students are able to use their knowledge and skills to develop concrete approaches for problem-solving and decision making to a selected problem statement, critically reflect on these approaches, assess their feasibility, to transfer them into a concrete form (physical model, prototypes, strategy paper, etc.) and to present this work in a creative way (role-plays, videos, exhibitions, etc.). - Self-competence: The students are able to plan their work effectively, efficiently and autonomously. By considering approaches from different disciplines they are able to make a judgment and form a personal opinion. In exchange with non-academic partners from business, politics, administration, non-governmental organisations and media they are able to communicate appropriately, present their results professionally and creatively and convince a critical audience. - Social competence: The students are able to work in multidisciplinary teams, i.e. they can reflect critically on their own discipline, debate with students from other disciplines and experts in a critical-constructive and respectful way and can relate their own positions to different intellectual approaches. They can assess how far they are able to actively make a contribution to society by using their personal and professional talents and skills as "Change Agents". - Remote collaboration competence: The students work in a hybrid setting blending physical and virtual communication and collaboration methods and tools. They experience the potential and limitations of remote collaboration. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The week is mainly about problem-solving and design thinking applied to the complex world of health and well-being. During ETH Week students will have the opportunity to work in small interdisciplinary groups, allowing them to critically analyse both their own approaches and those of other disciplines, and to integrate these into their work. While deepening their knowledge about sustainable urban development, students will be introduced to various methods and tools for generating creative ideas and understanding how different people are affected by each part of the system. In addition to lectures and literature, students will acquire knowledge via excursions into the real world, empirical observations, and conversations with researchers and experts. A key attribute of ETH Week is that students are expected to find their own problems, rather than just solve the problem that has been handed to them. Therefore, the first three days of the week will concentrate on identifying a problem the individual teams will work on, while the last two days are focused on generating solutions and communicating the team's ideas. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
851-0370-00L | Didactic Basics for Student Teaching Assistants | W | 1 credit | 1S | M. Lehner, B. Volk | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The course “Didactic Basics for Student Teaching Assistants” enhance Student Teaching Assistants (Student TAs) to develop knowledge, capability and confidence to effectively plan and teach courses and exercises. Participants get trained to think critically about students’ learning and create learning situations in which students are actively engaged. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | In this course Student Teaching Assistants will ... • reflect on their approach to teaching as well as their attitude towards teaching. • understand the basics of teaching and learning in the context of their subject. • consciously design the introduction of their course as well as the introduction of single teaching units. • apply classroom assessment techniques as formative assessments to measure the current status of their students. • develop a didactic concept according to the learning objectives. • conduct interactive sequences as learning activities. • give and get feedback from peers and self-reflect on their teaching practice. • feel confident to use methods for active learning scenarios in their classes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Content | The online course provide a range of relevant topics for developing teaching competences of Student Teaching Assistants: • Overview about how learning works. Based on these fundamentals of learning participants reflect on their role as Student TAs to feel comfortable in their new role as a teacher. • Plan an own lesson by introducing a class and locate it in the larger topic (methods: portal and informative introduction). • Develop learning activities in order to activate students (active learning methods). • Giving and also getting feedback. The participants integrate this topic also in their lesson plan. While working through the online course, Student TAs have the chance to reflect, exchange ideas with peers and plan their own teaching accordingly so that they feel confident in their role. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | Self-paced online course with a online/face-to-face consolidation workshop. Consolidation Workshops takes place online or in presence (you have the choice). Online: 2nd Nov, 13:15-16:00 OR HG D 18.1: 3rd Nov, 9:15-12:00 OR HG D 18.1: 3rd Nov, 13:15-16:00 You need to choose one of the dates and you will find registration details and a deadline in the Moodle course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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» Recommended Science in Perspective (Type B) for D-INFK | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
» see Science in Perspective: Type A: Enhancement of Reflection Capability | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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» see Science in Perspective: Language Courses ETH/UZH | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
252-0500-00L | Bachelor's Thesis ![]() | O | 10 credits | 21D | Professors | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Abstract | The Bachelor thesis is the final requirement of the BSc program and is supervised by one of the D-INFK professors. The thesis encourages students to show and produce a scientifically structured work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Learning objective | In their BSc thesis students should demonstrate their ability to carry out independent, structured scientific work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Prerequisites / Notice | The supervisor of the thesis defines the task, start and end date. A written report will be prepared on the scientific studies carried out, followed by a final presentation. The thesis must be handed in within 6 months. |