Search result: Catalogue data in Autumn Semester 2023
Computational Science and Engineering Bachelor  | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
 ElectivesIn the ‘electives’ subcategory, at least two course units must be successfully completed. | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Number | Title | Type | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0833-00L | Applied Finite Element Analysis | W | 4 credits | 2V + 2U | B. Berisha, D. Mohr | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Most problems in engineering are of nonlinear nature. The nonlinearities are caused basically due to the nonlinear material behavior, contact conditions and instability of structures. The principles of the nonlinear Finite-Element-Method (FEM) will be introduced for treating such problems. The finite element program ABAQUS is introduced to investigate real engineering problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of the lecture is to provide the students with the fundamentals of the non linear Finite Element Method (FEM). The lecture focuses on the principles of the nonlinear Finite-Element-Method based on explicit and implicit formulations. Typical applications of the nonlinear Finite-Element-Methods are simulations of: - Crash - Collapse of structures - Material behavior (metals and rubber) - General forming processes Special attention will be paid to the modeling of the nonlinear material behavior, thermo-mechanical processes and processes with large plastic deformations. The ability to independently create a virtual model which describes the complex non linear systems will be acquired through accompanying exercises. These will include the Matlab programming of important model components such as constitutive equations. The FEM Program ABAQUS will be introduced to investigate real engineering problems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | - introduction into FEM - Fundamentals of continuum mechanics to characterize large plastic deformations - Elasto-plastic material models - Lagrange and Euler approaches - FEM implementation of constitutive equations - Element formulations - Implicit and explicit FEM methods - FEM formulations of coupled thermo-mechanical problems - Modeling of tool contact and the influence of friction - Solvers and convergence - Instability problems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture slides | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Bathe, K. J., Finite-Element-Procedures, Prentice-Hall, 1996 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0529-00L | Computational Mechanics II: Nonlinear FEA | W | 4 credits | 2V + 2U | L. De Lorenzis | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The course provides an introduction to non-linear finite element analysis. The treated sources of non-linearity are related to material properties (hyperelasticity, plasticity), kinematics (large deformations, instability problems) and boundary conditions (contact). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | To be able to address all major sources of non-linearity in theory and numerics, and to apply this knowledge to the solution of relevant problems in solid mechanics. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 1. Introduction: various sources of nonlinearities and implications for FEA. 2. Non-linear kinematics: large deformations, stability problems. 3. Non-linear material behavior: hyperelasticity, plasticity. 4. Non-linear boundary conditions: contact problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes will be provided. However, students are encouraged to take their own notes. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Mechanics 1, 2, Dynamics, Continuum Mechanics I and Introduction to FEA. Ideally also Continuum Mechanics II. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 263-2800-00L | Design of Parallel and High-Performance Computing  | W | 9 credits | 2V + 2U + 4A | T. Hoefler | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Advanced topics in parallel and high-performance computing. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large parallel high-performance software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | We will cover all aspects of high-performance computing ranging from architecture through programming up to algorithms. We will start with a discussion of caches and cache coherence in practical computer systems. We will dive into parallel programming concepts such as memory models, locks, and lock-free. We will cover performance modeling and parallel design principles as well as basic parallel algorithms. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | This class is intended for the Computer Science Masters curriculum. Students must have basic knowledge in programming in C as well as computer science theory. Students should be familiar with the material covered in the ETH computer science first-year courses "Parallele Programmierung (parallel programming)" and "Algorithmen und Datenstrukturen (algorithm and data structures)" or equivalent courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0102-00L | Discrete Event Systems | W | 6 credits | 4G | L. Josipovic, L. Vanbever, R. Wattenhofer | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Introduction to discrete event systems. We start out by studying popular models of discrete event systems. Then we analyze discrete event systems from an average-case and from a worst-case perspective, and study verification. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, Worst-Case Event Systems, Verification, Petri Nets. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Over the past few decades the rapid evolution of computing, communication, and information technologies has brought about the proliferation of new dynamic systems. A significant part of activity in these systems is governed by operational rules designed by humans. The dynamics of these systems are characterized by asynchronous occurrences of discrete events, some controlled (e.g. hitting a keyboard key, sending a message), some not (e.g. spontaneous failure, packet loss). The mathematical arsenal centered around differential equations that has been employed in systems engineering to model and study processes governed by the laws of nature is often inadequate or inappropriate for discrete event systems. The challenge is to develop new modeling frameworks, analysis techniques, design tools, testing methods, and optimization processes for this new generation of systems. In this lecture we give an introduction to discrete event systems. We start out the course by exploring the limits of what is computable and what is not. In doing so, we will consider three distinct models of computation which are often used to model discrete event systems: finite automata, push-down automata and Turing machines (ranked in terms of expressiveness power). In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an average-case perspective: we model discrete events as stochastic processes, and then apply continuous time markov chains and queueing theory for an understanding of the typical behavior of a system. Then we analyze discrete event systems from a worst-case perspective using the theory of online algorithms and adversarial queueing. In the last part of the course we introduce methods that allow to formally verify certain properties of Finite Automata and Petri Nets. These are some typical analysis questions we will look at: Do two given systems behave the same? Does a given system behave as intended? Does the system eventually enter a dangerous state? | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | 1. Regular Languages 2. Non-Regular Languages 3. Markov Chains 4. Stochastic Discrete Event Systems 5. Worst-Case Event Systems 6. Verification of Finite Automata 7. Petri Nets | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Available at https://disco.ethz.ch/courses/des/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | [bertsekas] Data Networks Dimitri Bersekas, Robert Gallager Prentice Hall, 1991, ISBN: 0132009161 [borodin] Online Computation and Competitive Analysis Allan Borodin, Ran El-Yaniv. Cambridge University Press, 1998 [burch] Symbolic Model Checking J. R. Burch, E. M. Clarke, K. L. McMillan, D. L. Dill, and L. J. Hwang Inf. Comput. 98, 2 (June 1992), pp. 142-170 [boudec] Network Calculus J.-Y. Le Boudec, P. Thiran Springer, 2001 [cassandras] Introduction to Discrete Event Systems Christos Cassandras, Stéphane Lafortune. Kluwer Academic Publishers, 1999, ISBN 0-7923-8609-4 [fiat] Online Algorithms: The State of the Art A. Fiat and G. Woeginger [hochbaum] Approximation Algorithms for NP-hard Problems (Chapter 13 by S. Irani, A. Karlin) D. Hochbaum [murata] Petri Nets: Properties, Analysis and Applications Tadao Murata Proceedings of the IEEE, vol. 99, issue 4, April 1989. pp. 541-580 [schickinger] Diskrete Strukturen (Band 2: Wahrscheinlichkeitstheorie und Statistik) T. Schickinger, A. Steger Springer, Berlin, 2001 [sipser] Introduction to the Theory of Computation Michael Sipser. PWS Publishing Company, 1996, ISBN 053494728X | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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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/ | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0147-10L | VLSI 3: Full-Custom Digital Circuit Design | W | 6 credits | 2V + 3U | C. Studer, O. Castañeda Fernández | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This third course in our VLSI series is concerned with full-custom digital integrated circuits. The goals include learning the design of digital circuits on the schematic, layout, gate, and register-transfer levels. The use of state-of-the-art CAD software (Cadence Virtuoso) in order to simulate, optimize, and characterize digital circuits is another important topic of this course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of this course, you will • understand the design of the main building blocks of state-of-the-art digital integrated circuits • be able to design and optimize digital integrated circuits on the schematic, layout, and gate levels • be able to use standard industry software (Cadence Virtuoso) for drawing, simulating, and characterizing digital circuits • understand the performance trade-offs between delay, area, and power consumption | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The third VLSI course begins with the basics of metal-oxide-semiconductor (MOS) field-effect transistors (FETs) and moves up the stack towards logic gates and increasingly complex digital circuit structures. The topics of this course include: • Nanometer MOSFETs • Static and dynamic behavior of complementary MOS (CMOS) inverters • CMOS gate design, sizing, and timing • Full-custom standard-cell design • Wire models and parasitics • Latch and flip-flop circuits • Gate-level timing analysis and optimization • Static and dynamic power consumption; low-power techniques • Alternative logic styles (dynamic logic, pass-transistor logic, etc.) • Arithmetic and logic circuits • Fixed-point and floating-point arithmetic • Synchronous and asynchronous design principles • Memory circuits (ROM, SRAM, and DRAM) • In- and near-memory processing architectures • Full-custom accelerator circuits for machine learning The exercises are concerned with schematic entry, layout, and simulation of digital integrated circuits using a disciplined standard-cell-based approach with Cadence Virtuoso. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | N. H. E. Weste and D. M Harris, CMOS VLSI Design: A Circuits and Systems Perspective (4th Ed.), Addison-Wesley | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | VLSI 3 can be taken in parallel with “VLSI 1: HDL-based design for FPGAs” and is designed to complement the topics of this course. Basic analog circuit knowledge is required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0417-00L | Information Theory I | W | 6 credits | 4G | A. Lapidoth | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The fundamentals of Information Theory including Shannon's source coding and channel coding theorems | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | T.M. Cover and J. Thomas, Elements of Information Theory (second edition) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0971-00L | Computational Psychiatry Please note that participation in this course and the practical sessions requires additional registration at: http://www.translationalneuromodeling.org/cpcourse/ | W | 3 credits | 4S | K. Stephan | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This six-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This course aims at bridging the gap between mathematical modelers and clinical neuroscientists by teaching computational techniques in the context of clinical applications. The hope is that the acquisition of a joint language and tool-kit will enable more effective communication and joint translational research between fields that are usually worlds apart. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This six-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. Furthermore, practical exercises provide in-depth exposure to different software packages. Please see http://www.translationalneuromodeling.org/cpcourse/ for details. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0209-00L | Algorithms, Probability, and Computing | W | 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. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0206-00L | Visual Computing | W | 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0543-01L | Computer Graphics | W | 8 credits | 3V + 2U + 2A | M. Gross, M. Papas | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course covers fundamental and advanced concepts of modern computer graphics. Students will learn the fundamentals of digital scene representations, advanced physically-based light transport algorithms for generating photorealistic images from these scene representations, and inverse rendering methods for recovering digital scene representations from captured images. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At the end of the course, the students will be able to build a rendering system based on path-tracing algorithms. The students will learn the principles of physically-based rendering and computer graphics. In addition, the course is intended to stimulate the student's curiosity to explore the field of computer graphics in subsequent classes or on their own. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | We will begin with an introduction to light emission and radiometric quantities, followed by an exploration of geometry representations and texture mapping. Next, we will mathematically formulate the physics of light transport and appearance modeling. Subsequently, we will introduce relevant concepts from Monte Carlo integration and develop path-tracing algorithms to solve these equations by simulating light transport for direct and global illumination due to hard surfaces and participating media, such as fog, smoke, and translucent objects. Moreover, we will present techniques for significantly improving path-tracing efficiency, including importance sampling, multiple importance sampling, stratified sampling, denoising, and acceleration data structures. The course lectures will conclude with an overview of image-based capture and rendering methods. Topics covered will include geometry reconstruction, material acquisition, differentiable rendering, and image-based rendering. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | no | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Books: Physically Based Rendering: From Theory to Implementation High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting Multiple view geometry in Computer Vision | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Prerequisites: Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, and the Visual Computing course are recommended. The programming assignments will be in C++. This will not be taught in the class. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 252-0546-00L | Physically-Based Simulation in Computer Graphics | W | 5 credits | 2V + 1U + 1A | S. Coros, B. Thomaszewski | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | This lecture provides an introduction to physically-based animation in computer graphics and gives an overview of fundamental methods and algorithms. The practical exercises include three assignments which are to be solved in small groups. In an addtional course project, topics from the lecture will be implemented into a 3D game or a comparable application. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The lecture covers topics in physically-based modeling, such as particle systems, mass-spring models, finite difference and finite element methods. These approaches are used to represent and simulate deformable objects or fluids with applications in animated movies, 3D games and medical systems. Furthermore, the lecture covers topics such as rigid body dynamics, collision detection, and character animation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Fundamentals of calculus and physics, basic concepts of algorithms and data structures, basic programming skills in C++. Knowledge on numerical mathematics as well as ordinary and partial differential equations is an asset, but not required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0834-00L | Information Systems for Engineers | W | 4 credits | 2V + 1U | G. Fourny | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course provides the basics of relational databases from the perspective of the user. We will discover why tables are so incredibly powerful to express relations, learn the SQL query language, and how to make the most of it. The course also covers support for data cubes (analytics). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Do you want to be able to query your own data productively and efficiently in your future semester projects, bachelor's thesis, master thesis, or PhD thesis? Are you looking for something beyond the Python+Pandas hype? This courses teaches you how to do so as well as the dos and don'ts. This lesson is complementary with Big Data for Engineers as they cover different time periods of database history and practices -- you can take them in any order, even though it might be more enjoyable to take this lecture first. After visiting this course, you will be capable to: 1. Explain, in the big picture, how a relational database works and what it can do in your own words. 2. Explain the relational data model (tables, rows, attributes, primary keys, foreign keys), formally and informally, including the relational algebra operators (select, project, rename, all kinds of joins, division, cartesian product, union, intersection, etc). 3. Perform non-trivial reading SQL queries on existing relational databases, as well as insert new data, update and delete existing data. 4. Design new schemas to store data in accordance to the real world's constraints, such as relationship cardinality 5. Explain what bad design is and why it matters. 6. Adapt and improve an existing schema to make it more robust against anomalies, thanks to a very good theoretical knowledge of what is called "normal forms". 7. Understand how indices work (hash indices, B-trees), how they are implemented, and how to use them to make queries faster. 8. Access an existing relational database from a host language such as Java, using bridges such as JDBC. 9. Explain what data independence is all about and didn't age a bit since the 1970s. 10. Explain, in the big picture, how a relational database is physically implemented. 11. Know and deal with the natural syntax for relational data, CSV. 12. Explain the data cube model including slicing and dicing. 13. Store data cubes in a relational database. 14. Map cube queries to SQL. 15. Slice and dice cubes in a UI. And of course, you will think that tables are the most wonderful object in the world. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Using a relational database ================= 1. Introduction 2. The relational model 3. Data definition with SQL 4. The relational algebra 5. Queries with SQL Taking a relational database to the next level ================= 6. Database design theory 7. Databases and host languages 8. Databases and host languages 9. Indices and optimization 10. Database architecture and storage Analytics on top of a relational database ================= 12. Data cubes Outlook ================= 13. Outlook | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Lecture material (slides). - Book: "Database Systems: The Complete Book", H. Garcia-Molina, J.D. Ullman, J. Widom (It is not required to buy the book, as the library has it) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The lecture is hybrid, meaning you can attend with us in the lecture hall, or on Zoom, or watch the recordings on YouTube later. Exercise sessions are in presence. For non-CS/DS students only, BSc and MSc Elementary knowledge of set theory and logics Knowledge as well as basic experience with a programming language such as Pascal, C, C++, Java, Haskell, Python | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 401-3627-00L | High-Dimensional Statistics | W | 4 credits | 2V | P. L. Bühlmann | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | "High-Dimensional Statistics" deals with modern methods and theory for statistical inference when the number of unknown parameters is of much larger order than sample size. Statistical estimation and algorithms for complex models and aspects of multiple testing will be discussed. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Knowledge of methods and basic theory for high-dimensional statistical inference | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Lasso and Group Lasso for high-dimensional linear and generalized linear models; Additive models and many smooth univariate functions; Non-convex loss functions and l1-regularization; Stability selection, multiple testing and construction of p-values; Undirected graphical modeling | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Peter Bühlmann and Sara van de Geer (2011). Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer Verlag. ISBN 978-3-642-20191-2. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Knowledge of basic concepts in probability theory, and intermediate knowledge of statistics (e.g. a course in linear models or computational statistics). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 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 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 401-3901-00L | Linear & Combinatorial Optimization | W | 10 credits | 4V + 2U | R. Zenklusen | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Mathematical treatment of optimization techniques for linear and combinatorial optimization problems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The goal of this course is to get a thorough understanding of various classical mathematical optimization techniques for linear and combinatorial optimization problems, with an emphasis on polyhedral approaches. In particular, we want students to develop a good understanding of some important problem classes in the field, of structural mathematical results linked to these problems, and of solution approaches based on such structural insights. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Key topics include: - Linear programming and polyhedra; - Flows and cuts; - Combinatorial optimization problems and polyhedral techniques; - Equivalence between optimization and separation. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | - Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018. - Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes. - Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993. - Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Solid background in linear algebra. Former course title: Mathematical Optimization. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 402-2203-01L | Classical Mechanics | W | 7 credits | 4V + 2U | P. Hintz | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | A conceptual introduction to theoretical physics: Newtonian mechanics, central force problem, oscillations, Lagrangian mechanics, symmetries and conservation laws, Hamiltonian mechanics, canonical transformations, Hamilton-Jacobi equation, spinning top, relativistic space-time structure. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Fundamental understanding of the description of Mechanics in the Lagrangian and Hamiltonian formulation. Detailed understanding of important applications, in particular, the Kepler problem, the physics of rigid bodies (spinning top) and of oscillatory systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-1033-00L | Neuromorphic Engineering I Registration in this class requires the permission of the instructors. Class size will be limited to available lab spots. Preference is given to students that require this class as part of their major. Information for UZH students: Enrolment to this course unit only possible at ETH. No enrolment to module INI404 at UZH. Please mind the ETH enrolment deadlines for UZH students: Link | W | 6 credits | 2V + 3U | T. Delbrück, S.‑C. Liu, M. Payvand | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course covers analog circuits with emphasis on neuromorphic engineering: MOS transistors in CMOS technology, static circuits, dynamic circuits, systems (silicon neuron, silicon retina, silicon cochlea) with an introduction to multi-chip systems. The lectures are accompanied by weekly laboratory sessions. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Understanding of the characteristics of neuromorphic circuit elements. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Neuromorphic circuits are inspired by the organizing principles of biological neural circuits. Their computational primitives are based on physics of semiconductor devices. Neuromorphic architectures often rely on collective computation in parallel networks. Adaptation, learning and memory are implemented locally within the individual computational elements. Transistors are often operated in weak inversion (below threshold), where they exhibit exponential I-V characteristics and low currents. These properties lead to the feasibility of high-density, low-power implementations of functions that are computationally intensive in other paradigms. Application domains of neuromorphic circuits include silicon retinas and cochleas for machine vision and audition, real-time emulations of networks of biological neurons, and the development of autonomous robotic systems. This course covers devices in CMOS technology (MOS transistor below and above threshold, floating-gate MOS transistor, phototransducers), static circuits (differential pair, current mirror, transconductance amplifiers, etc.), dynamic circuits (linear and nonlinear filters, adaptive circuits), systems (silicon neuron, silicon retina and cochlea) and an introduction to multi-chip systems that communicate events analogous to spikes. The lectures are accompanied by weekly laboratory sessions on the characterization of neuromorphic circuits, from elementary devices to systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | S.-C. Liu et al.: Analog VLSI Circuits and Principles; various publications. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Particular: The course is highly recommended for those who intend to take the spring semester course 'Neuromorphic Engineering II', that teaches the conception, simulation, and physical layout of such circuits with chip design tools. Prerequisites: Background in basics of semiconductor physics helpful, but not required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 327-1201-00L | Transport Phenomena I Does not take place this semester. | W | 5 credits | 4G | J. Vermant | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Phenomenological approach to "Transport Phenomena" based on balance equations supplemented by thermodynamic considerations to formulate the undetermined fluxes in the local species mass, momentum, and energy balance equations; Solutions of a few selected problems relevant to materials science and engineering both analytical and using numerical methods. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The teaching goals of this course are on five different levels: (1) Deep understanding of fundamentals: local balance equations, constitutive equations for fluxes, entropy balance, interfaces, idea of dimensionless numbers and scaling, ... (2) Ability to use the fundamental concepts in applications (3) Insight into the role of boundary conditions (mainly part 2) (4) Knowledge of a number of applications. (5) Flavor of numerical techniques: finite elements and finite differences. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Part 1 Approach to Transport Phenomena Equilibrium Thermodynamics Balance Equations Forces and Fluxes Applications 1. Measuring Transport Coefficients 2. Fluid mechanics 3. combined heat and flow | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The course is based on the book D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018) and the book by W. M. Deen, Analysis of Transport Phenomena (Oxford University Press, 1998) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | 1. D. C. Venerus and H. C. Öttinger, A Modern Course in Transport Phenomena (Cambridge University Press, 2018) 2. R. B. Bird, W. E. Stewart, and E. N. Lightfoot, Transport Phenomena, 2nd Ed. (Wiley, 2001) 3. L.G. Leal, Advanced Transport Phenomena (Oxford University Press, 2011) 4. W. M. Deen, Analysis of Transport Phenomena (Oxford University Press, 1998) 5. R. B. Bird, Five Decades of Transport Phenomena (Review Article), AIChE J. 50 (2004) 273-287 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Complex numbers. Vector analysis (integrability; Gauss' divergence theorem). Laplace and Fourier transforms. Ordinary differential equations (basic ideas). Linear algebra (matrices; functions of matrices; eigenvectors and eigenvalues; eigenfunctions). Probability theory (Gaussian distributions; Poisson distributions; averages; moments; variances; random variables). Numerical mathematics (integration). Equilibrium thermodynamics (Gibbs' fundamental equation; thermodynamic potentials; Legendre transforms). Maxwell equations. Programming and simulation techniques (Matlab, Monte Carlo simulations). | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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