Search result: Catalogue data in Autumn Semester 2021
Computational Science and Engineering Bachelor  
Electives In the ‘electives’ subcategory, at least two course units must be successfully completed.  
Number  Title  Type  ECTS  Hours  Lecturers  

151070900L  Stochastic Methods for Engineers and Natural Scientists  W  4 credits  4G  D. W. MeyerMassetti  
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.  
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  Markovchain Monte Carlo sampling  Controlvariate and multilevel Monte Carlo estimation 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 FokkerPlanck 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  
Fostered competencies 
 
151031700L  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.  
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 netbased 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 freeform 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.  
Fostered competencies 
 
151083300L  Applied Finite Element Analysis  W  4 credits  2V + 2U  B. Berisha, N. Manopulo  
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 FiniteElementMethod (FEM) will be introduced for treating such problems. The finite element program ABAQUS is introduced to investigate real engineering problems.  
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 FiniteElementMethod based on explicit and implicit formulations. Typical applications of the nonlinear FiniteElementMethods 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, thermomechanical 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  Elastoplastic material models  Lagrange and Euler approaches  FEM implementation of constitutive equations  Element formulations  Implicit and explicit FEM methods  FEM formulations of coupled thermomechanical problems  Modeling of tool contact and the influence of friction  Solvers and convergence  Instability problems  
Lecture notes  Lecture slides  
Literature  Bathe, K. J., FiniteElementProcedures, PrenticeHall, 1996  
151052900L  Computational Mechanics II: Nonlinear FEA  W  4 credits  2V + 2U  L. De Lorenzis  
Abstract  The course provides an introduction to nonlinear finite element analysis. The treated sources of nonlinearity are related to material properties (hyperelasticity, plasticity), kinematics (large deformations, instability problems) and boundary conditions (contact).  
Objective  To be able to address all major sources of nonlinearity 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. Nonlinear kinematics: large deformations, stability problems. 3. Nonlinear material behavior: hyperelasticity, plasticity. 4. Nonlinear 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.  
263280000L  Design of Parallel and HighPerformance Computing Number of participants limited to 125.  W  9 credits  3V + 2U + 3A  T. Hoefler, M. Püschel  
Abstract  Advanced topics in parallel and highperformance computing.  
Objective  Understand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large parallel highperformance 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 highperformance 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 lockfree. 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 firstyear courses "Parallele Programmierung (parallel programming)" and "Algorithmen und Datenstrukturen (algorithm and data structures)" or equivalent courses.  
227010200L  Discrete Event Systems  W  6 credits  4G  R. Jacob, L. Vanbever, R. Wattenhofer  
Abstract  Introduction to discrete event systems. We start out by studying popular models of discrete event systems. In the second part of the course we analyze discrete event systems from an averagecase and from a worstcase perspective. Topics include: Automata and Languages, Specification Models, Stochastic Discrete Event Systems, WorstCase Event Systems, Verification, Network Calculus.  
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 studying popular models of discrete event systems, such as automata and Petri nets. In the second part of the course we analyze discrete event systems. We first examine discrete event systems from an averagecase perspective: we model discrete events as stochastic processes, and then apply Markov chains and queuing theory for an understanding of the typical behavior of a system. In the last part of the course we analyze discrete event systems from a worstcase perspective using the theory of online algorithms and adversarial queuing.  
Content  1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. WorstCase Event Systems 7. Network Calculus  
Lecture notes  Available  
Literature  [bertsekas] Data Networks Dimitri Bersekas, Robert Gallager Prentice Hall, 1991, ISBN: 0132009161 [borodin] Online Computation and Competitive Analysis Allan Borodin, Ran ElYaniv. Cambridge University Press, 1998 [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 0792386094 [fiat] Online Algorithms: The State of the Art A. Fiat and G. Woeginger [hochbaum] Approximation Algorithms for NPhard Problems (Chapter 13 by S. Irani, A. Karlin) D. Hochbaum [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  
227011600L  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 industrialquality circuits.  
Objective  Understand VeryLargeScale Integrated Circuits (VLSI chips), ApplicationSpecific Integrated Circuits (ASIC), and FieldProgrammable GateArrays (FPGA). Know their organization and be able to identify suitable application areas. Become fluent in frontend design from architectural conception to gatelevel 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 industrialquality 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 systemlevel 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 fieldprogrammable 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.  Assertionbased 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 gatelevel 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: "TopDown Digital VLSI Design, from Architectures to GateLevel 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://iisstudents.ee.ethz.ch/lectures/vlsii/  
227014710L  VLSI 3: FullCustom Digital Circuit Design  W  6 credits  2V + 3U  C. Studer, O. Castañeda Fernández  
Abstract  This third course in our VLSI series is concerned with fullcustom digital integrated circuits. The goals are to learn how to design digital circuits on the schematic, layout, gate, and registertransfer levels. The use of stateoftheart CAD software (Cadence Virtuoso) in order to simulate, optimize, and characterize digital circuits is another important topic of this course.  
Objective  At the end of this course you will  understand how the main building blocks of stateoftheart digital integrated circuits are designed  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 tradeoffs between speed, area, and power consumption  
Content  The third VLSI course begins with the basics of metaloxidesemiconductor (MOS) fieldeffect 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 • Fullcustom standardcell design • Wire models and parasitics • Latch and flipflop circuits • Gatelevel timing analysis and optimization • Static and dynamic power consumption; lowpower techniques • Alternative logic styles (dynamic logic, passtransistor logic, etc.) • Arithmetic and logic circuits • Fixedpoint and floatingpoint arithmetic • Memory circuits (ROM, SRAM, and DRAM) • In and nearmemory processing architectures • Fullcustom accelerator circuits for machine learning The exercises are concerned with schematic entry, layout, and simulation of digital integrated circuits using a disciplined standardcellbased approach with Cadence Virtuoso.  
Literature  N. H. E. Weste and D. M Harris, CMOS VLSI Design: A Circuits and Systems Perspective (4th Ed.), AddisonWesley  
Prerequisites / Notice  VLSI3 can be taken in parallel with “VLSI1: HDL based design for FPGAs” and is designed to complement the topics of this course. Basic analog circuit knowledge is required.  
227014800L  VLSI III: Test and Fabrication of VLSI Circuits Does not take place this semester.  W  6 credits  4G  L. Benini  
Abstract  In this course, we will cover how modern microchips are fabricated, and we will focus on methods and tools to uncover fabrication defects, if any, in these microchips. As part of the exercises, students will get to work on an industrial 1 million dollar automated test equipment.  
Objective  Learn about modern IC manufacturing methodologies, understand the problem of IC testing. Cover the basic methods, algorithms and techniques to test circuits in an efficient way. Learn about practical aspects of IC testing and apply what you learn in class using a stateofthe art tester.  
Content  In this course we will deal with modern integrated circuit (IC) manufacturing technology and cover topics such as:  Today's nanometer CMOS fabrication processes (HKMG).  Optical and post optical Photolithography.  Potential alternatives to CMOS technology and MOSFET devices.  Evolution paths for design methodology.  Industrial roadmaps for the future evolution of semiconductor technology (ITRS). If you want to earn money by selling ICs, you will have to deliver a product that will function properly with a very large probability. The main emphasis of the lecture will be discussing how this can be achieved. We will discuss fault models and practical techniques to improve testability of VLSI circuits. At the IIS we have a stateoftheart automated test equipment (Advantest SoC V93000) that we will make available for in class exercises and projects. At the end of the lecture you will be able to design stateofthe art digital integrated circuits such as to make them testable and to use automatic test equipment (ATE) to carry out the actual testing. During the first weeks of the course there will be weekly practical exercises where you will work in groups of two. For the last 5 weeks of the class students will be able to choose a class project that can be:  The test of their own chip developed during a previous semester thesis  Developing new setups and measurement methods in C++ on the tester  Helping to debug problems encountered in previous microchips by IIS. Half of the oral exam will consist of a short presentation on this class project.  
Lecture notes  Main course book: "Essentials of Electronic Testing for Digital, Memory and MixedSignal VLSI Circuits" by Michael L. Bushnell and Vishwani D. Agrawal, Springer, 2004. This book is available online within ETH through http://link.springer.com/book/10.1007%2Fb117406  
Prerequisites / Notice  Although this is the third part in a series of lectures on VLSI design, you can follow this course even if you have not visited VLSI I and VLSI II lectures. An interest in integrated circuit design, and basic digital circuit knowledge is required though. Course website: https://iisstudents.ee.ethz.ch/lectures/vlsiiii/  
227041700L  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 equipartition property, Huffman coding, channel capacity, the channel coding theorem, the sourcechannel separation theorem, and feedback capacity.  
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 equipartition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the sourcechannel separation theorem, feedback capacity  
Literature  T.M. Cover and J. Thomas, Elements of Information Theory (second edition)  
227042700L  Signal Analysis, Models, and Machine Learning Does not take place this semester. This course was replaced by "Introduction to Estimation and Machine Learning" and "Advanced Signal Analysis, Modeling, and Machine Learning".  W  6 credits  4G  H.‑A. Loeliger  
Abstract  Mathematical methods in signal processing and machine learning. I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity. II. Learning linear and nonlinear functions and filters: neural networks, kernel methods. III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, Gaussian models with sparse events.  
Objective  The course is an introduction to some basic topics in signal processing and machine learning.  
Content  Part I  Linear Signal Representation and Approximation: Hilbert spaces, least squares and LMMSE estimation, projection and estimation by linear filtering, learning linear functions and filters, L2 regularization, L1 regularization and sparsity, singularvalue decomposition and pseudoinverse, principalcomponents analysis. Part II  Learning Nonlinear Functions: fundamentals of learning, neural networks, kernel methods. Part III  Structured Statistical Models and Message Passing Algorithms: hidden Markov models, factor graphs, Gaussian message passing, Kalman filter and recursive least squares, Monte Carlo methods, parameter estimation, expectation maximization, linear Gaussian models with sparse events.  
Lecture notes  Lecture notes.  
Prerequisites / Notice  Prerequisites:  local bachelors: course "DiscreteTime and Statistical Signal Processing" (5. Sem.)  others: solid basics in linear algebra and probability theory  
227097100L  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 sixday course teaches stateoftheart methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decisionmaking) 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.  
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 toolkit will enable more effective communication and joint translational research between fields that are usually worlds apart.  
Content  This sixday course teaches stateoftheart methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decisionmaking) 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 indepth exposure to different software packages. Please see http://www.translationalneuromodeling.org/cpcourse/ for details.  
252041700L  Randomized Algorithms and Probabilistic Methods  W  10 credits  3V + 2U + 4A  A. Steger  
Abstract  Las Vegas & Monte Carlo algorithms; inequalities of Markov, Chebyshev, Chernoff; negative correlation; Markov chains: convergence, rapidly mixing; generating functions; Examples include: min cut, median, balls and bins, routing in hypercubes, 3SAT, card shuffling, random walks  
Objective  After this course students will know fundamental techniques from probabilistic combinatorics for designing randomized algorithms and will be able to apply them to solve typical problems in these areas.  
Content  Randomized Algorithms are algorithms that "flip coins" to take certain decisions. This concept extends the classical model of deterministic algorithms and has become very popular and useful within the last twenty years. In many cases, randomized algorithms are faster, simpler or just more elegant than deterministic ones. In the course, we will discuss basic principles and techniques and derive from them a number of randomized methods for problems in different areas.  
Lecture notes  Yes.  
Literature   Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995)  Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005)  
252020600L  Visual Computing  W  8 credits  4V + 3U  S. Coros, 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.  
Objective  This course provides an indepth 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, 19942005  
252054301L  Computer Graphics Does not take place this semester.  W  8 credits  3V + 2U + 2A  
Abstract  This course covers some of the fundamental concepts of computer graphics generation of photorealistic images from digital representations of 3D scenes and imagebased methods for recovering digital scene representations from captured images.  
Objective  At the end of the course the students will be able to build a rendering system. The students will study the basic principles of rendering and image synthesis. In addition, the course is intended to stimulate the students' curiosity to explore the field of computer graphics in subsequent courses or on their own.  
Content  This course covers fundamental concepts of modern computer graphics. Students will learn about 3D object representations and the details of how to generate photorealistic images from digital representations of 3D scenes. Starting with an introduction to 3D shape modeling, geometry representation and texture mapping, we will move on to the physics of light transport, acceleration structures, appearance modeling and Monte Carlo integration. We will apply these principles for computing light transport of direct and global illumination due to surfaces and participating media. We will end with an overview of modern imagebased capture and image synthesis methods, covering topics such as geometry and material capture, lightfields and depthimage based rendering.  
Lecture notes  no  
Literature  Books: High Dynamic Range Imaging: Acquisition, Display, and ImageBased Lighting Multiple view geometry in computer vision Physically Based Rendering: From Theory to Implementation  
Prerequisites / Notice  Prerequisites: Fundamentals of calculus and linear algebra, basic concepts of algorithms and data structures, programming skills in C++, Visual Computing course recommended. The programming assignments will be in C++. This will not be taught in the class.  
252054600L  PhysicallyBased Simulation in Computer Graphics  W  5 credits  2V + 1U + 1A  V. da Costa de Azevedo, B. Solenthaler, B. Thomaszewski  
Abstract  This lecture provides an introduction to physicallybased 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.  
Objective  This lecture provides an introduction to physicallybased 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 physicallybased modeling, such as particle systems, massspring 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.  
252083400L  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).  
Objective  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 nontrivial 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, Btrees), 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. GarciaMolina, J.D. Ullman, J. Widom (It is not required to buy the book, as the library has it)  
Prerequisites / Notice  For nonCS/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  
401362700L  HighDimensional Statistics  W  4 credits  2V  P. L. Bühlmann  
Abstract  "HighDimensional 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.  
Objective  Knowledge of methods and basic theory for highdimensional statistical inference  
Content  Lasso and Group Lasso for highdimensional linear and generalized linear models; Additive models and many smooth univariate functions; Nonconvex loss functions and l1regularization; Stability selection, multiple testing and construction of pvalues; Undirected graphical modeling  
Literature  Peter Bühlmann and Sara van de Geer (2011). Statistics for HighDimensional Data: Methods, Theory and Applications. Springer Verlag. ISBN 9783642201912.  
Prerequisites / Notice  Knowledge of basic concepts in probability theory, and intermediate knowledge of statistics (e.g. a course in linear models or computational statistics).  
401462300L  Time Series Analysis Does not take place this semester.  W  6 credits  3G  F. Balabdaoui  
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.  
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  
401390100L  Linear & Combinatorial Optimization  W  11 credits  4V + 2U  R. Zenklusen  
Abstract  Mathematical treatment of optimization techniques for linear and combinatorial optimization problems.  
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.  
Fostered competencies 

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