Suchergebnis: Katalogdaten im Herbstsemester 2018
Rechnergestützte Wissenschaften Bachelor | ||||||
Für alle Studienreglemente | ||||||
Wahlfächer Von den angebotenen Wahlfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden. | ||||||
Nummer | Titel | Typ | ECTS | Umfang | Dozierende | |
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» siehe auch Angebot im Abschnitt Vertiefungsgebiete | ||||||
» Wahlfächer (RW Master) | ||||||
151-0113-00L | Applied Fluid Dynamics | W | 4 KP | 2V + 1U | J.‑P. Kunsch | |
Kurzbeschreibung | Angewandte Fluiddynamik Die Methoden der Fluiddynamik spielen eine wichtige Rolle bei der Beschreibung einer Ereigniskette, welche die Freisetzung, Ausbreitung und Verdünnung gefährlicher Fluide in der Umgebung beinhaltet. Tunnellüftungssysteme und -strategien werden vorgestellt, welche strengen Anforderungen während des Normalbetriebs und während eines Brandes genügen müssen. | |||||
Lernziel | Allgemein anwendbare Methoden der Strömungslehre und der Gasdynamik sollen hier an ausgewählten, aktuellen Fallbeispielen illustriert und geübt werden. | |||||
Inhalt | Bei der Auslegung von umweltgerechten Prozess- und Verbrennungsanlagen sowie der Auswahl von sicheren Transport- und Lagerungsvarianten gefährlicher Stoffe wird häufig auf die Methoden der Fluiddynamik zurückgegriffen. Bei Unfällen, aber auch beim Normalbetrieb, können gefährliche Gase und Flüssigkeiten freigesetzt und durch den Wind oder Wasserströmungen weitertransportiert werden. Zu den vielfältigen möglichen Schadenseinwirkungen gehören z.B. Feuer und Explosionen bei zündfähigen Gemischen. Behandelte Themen sind u.a.: Ausströmen von flüssigen und gasförmigen Stoffen aus Behältern und Leitungen, Verdunstung aus Lachen und Verdampfung bei druckgelagerten Gasen, Ausbreitung und Verdünnung von Abgasfahnen im Windfeld, Deflagrations- und Detonationsvorgänge bei zündfähigen Gasen, Feuerbälle bei druckgelagerten Gasen, Schadstoff- und Rauchgasausbreitung in Tunnels (Tunnelbrände usw.). | |||||
Skript | nicht verfügbar | |||||
Voraussetzungen / Besonderes | Voraussetzungen: Fluiddynamik I und II, Thermodynamik I und II | |||||
151-0709-00L | Stochastic Methods for Engineers and Natural Scientists Number of participants limited to 45. | W | 4 KP | 3G | D. W. Meyer-Massetti | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | - 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 - Polynomial chaos and other expansion methods All topics are illustrated with engineering applications. | |||||
Skript | Detailed lecture notes will be provided. | |||||
Literatur | 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 | |||||
151-0317-00L | Visualization, Simulation and Interaction - Virtual Reality II | W | 4 KP | 3G | A. Kunz | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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 | |||||
Skript | The handout is available in German and English. | |||||
Voraussetzungen / Besonderes | Prerequisites: "Visualization, Simulation and Interaction - Virtual Reality I" is recommended. Didactical concept: The course consists of lectures and exercises. | |||||
151-0833-00L | Principles of Nonlinear Finite-Element-Methods | W | 5 KP | 2V + 2U | N. Manopulo, B. Berisha | |
Kurzbeschreibung | Die meisten Problemstellungen im Ingenieurwesen sind nichtlinearer Natur. Die Nichtlinearitäten werden hauptsächlich durch nichtlineares Werkstoffverhalten, Kontaktbedingungen und Strukturinstabilitäten hervorgerufen. Im Rahmen dieser Vorlesung werden die theoretischen Grundlagen der nichtlinearen Finite-Element-Methoden zur Lösung von solchen Problemstellungen vermittelt. | |||||
Lernziel | Das Ziel der Vorlesung ist die Vermittlung von Grundkenntnissen der nichtlinearen Finite-Elemente-Methode (FEM). Der Fokus der Vorlesung liegt bei der Vermittlung der theoretischen Grundlagen der nichtlinearen FE-Methoden für implizite und explizite Formulierungen. Typische Anwendungen der nichtlinearen FE-Methode sind Simulationen von: - Crash - Kollaps von Strukturen - Materialien aus der Biomechanik (Softmaterials) - allgemeinen Umformprozessen Insbesondere wird die Modellierung des nichtlinearem Werkstoffverhalten, thermomechanischen Vorgängen und Prozessen mit grossen plastischen Deformationen behandelt. Im Rahmen von begleitenden Uebungen wird die Fähigkeit erworben, selber virtuelle Modelle zur Beschreibung von komplexen nichtlinearen Systemen aufzubauen. Wichtige Modelle wie z.B. Stoffgesetze werden in Matlab programmiert. | |||||
Inhalt | - Kontinuumsmechanische Grundlagen zur Beschreibung grosser plastischer Deformationen - Elasto-plastische Werkstoffmodelle - Aufdatiert-Lagrange- (UL), Euler- und Gemischt-Euler-Lagrange (ALE) Betrachtungsweisen - FEM-Implementation von Stoffgesetzen - Elementformulierungen - Implizite und explizite FEM-Methoden - FEM-Formulierung des gekoppelten thermo-mechanischen Problems - Modellierung des Werkzeugkontaktes und von Reibungseinflüssen - Gleichungslöser und Konvergenz - Modellierung von Rissausbreitungen - Vorstellung erweiterter FE-Verfahren | |||||
Skript | ja | |||||
Literatur | Bathe, K. J., Finite-Elemente-Methoden, Springer-Verlag, 2002 | |||||
Voraussetzungen / Besonderes | Bei einer grossen Anzahl von Studenten werden bei Bedarf zwei Übungstermine angeboten. | |||||
263-2800-00L | Design of Parallel and High-Performance Computing | W | 7 KP | 3V + 2U + 1A | T. Hoefler, M. Püschel | |
Kurzbeschreibung | Advanced topics in parallel / concurrent programming. | |||||
Lernziel | Understand concurrency paradigms and models from a higher perspective and acquire skills for designing, structuring and developing possibly large concurrent software systems. Become able to distinguish parallelism in problem space and in machine space. Become familiar with important technical concepts and with concurrency folklore. | |||||
227-0102-00L | Diskrete Ereignissysteme | W | 6 KP | 4G | L. Thiele, L. Vanbever, R. Wattenhofer | |
Kurzbeschreibung | Einführung in Diskrete Ereignissysteme (DES). Zuerst studieren wir populäre Modelle für DES. Im zweiten Teil analysieren wir DES, aus einer Average-Case und einer Worst-Case Sicht. Stichworte: Automaten und Sprachen, Spezifikationsmodelle, Stochastische DES, Worst-Case Ereignissysteme, Verifikation, Netzwerkalgebra. | |||||
Lernziel | 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 average-case 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 worst-case perspective using the theory of online algorithms and adversarial queuing. | |||||
Inhalt | 1. Introduction 2. Automata and Languages 3. Smarter Automata 4. Specification Models 5. Stochastic Discrete Event Systems 6. Worst-Case Event Systems 7. Network Calculus | |||||
Skript | Available | |||||
Literatur | [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 [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 [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 | |||||
227-0116-00L | VLSI I: From Architectures to VLSI Circuits and FPGAs | W | 6 KP | 5G | F. K. Gürkaynak, L. Benini | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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 VHDL or 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 VHDL and with industrial Electronic Design Automation (EDA) tools. | |||||
Inhalt | 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. - VLSI and 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. - VHDL and SystemVerilog compared. - VHDL (IEEE standard 1076) for simulation and synthesis. - A suitable nine-valued logic system (IEEE standard 1164). - 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 digital ICs with VHDL. They write testbenches for simulation purposes and synthesize gate-level netlists for VLSI chips and FPGAs. Commercial EDA software by leading vendors is being used throughout. | |||||
Skript | Textbook and all further documents in English. | |||||
Literatur | H. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303. | |||||
Voraussetzungen / Besonderes | 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: Link | |||||
227-0148-00L | VLSI III: Test and Fabrication of VLSI Circuits | W | 6 KP | 4G | F. K. Gürkaynak, L. Benini | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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 state-of-the art tester. | |||||
Inhalt | 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 state-of-the-art 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 state-of-the 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. | |||||
Skript | Main course book: "Essentials of Electronic Testing for Digital, Memory and Mixed-Signal VLSI Circuits" by Michael L. Bushnell and Vishwani D. Agrawal, Springer, 2004. This book is available online within ETH through Link | |||||
Voraussetzungen / Besonderes | 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: Link | |||||
227-0447-00L | Image Analysis and Computer Vision | W | 6 KP | 3V + 1U | L. Van Gool, O. Göksel, E. Konukoglu | |
Kurzbeschreibung | Light and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks. | |||||
Lernziel | Overview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises. | |||||
Inhalt | This course aims at offering a self-contained account of computer vision and its underlying concepts, including the recent use of deep learning. The first part starts with an overview of existing and emerging applications that need computer vision. It shows that the realm of image processing is no longer restricted to the factory floor, but is entering several fields of our daily life. First the interaction of light with matter is considered. The most important hardware components such as cameras and illumination sources are also discussed. The course then turns to image discretization, necessary to process images by computer. The next part describes necessary pre-processing steps, that enhance image quality and/or detect specific features. Linear and non-linear filters are introduced for that purpose. The course will continue by analyzing procedures allowing to extract additional types of basic information from multiple images, with motion and 3D shape as two important examples. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed. A major part at the end is devoted to deep learning and AI-based approaches to image analysis. Its main focus is on object recognition, but also other examples of image processing using deep neural nets are given. | |||||
Skript | Course material Script, computer demonstrations, exercises and problem solutions | |||||
Voraussetzungen / Besonderes | Prerequisites: Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Python and Linux. The course language is English. | |||||
227-0417-00L | Information Theory I | W | 6 KP | 4G | A. Lapidoth | |
Kurzbeschreibung | 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. | |||||
Lernziel | The fundamentals of Information Theory including Shannon's source coding and channel coding theorems | |||||
Inhalt | 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 | |||||
Literatur | T.M. Cover and J. Thomas, Elements of Information Theory (second edition) | |||||
227-0427-00L | Signal Analysis, Models, and Machine Learning | W | 6 KP | 4G | H.‑A. Loeliger | |
Kurzbeschreibung | 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. | |||||
Lernziel | The course is an introduction to some basic topics in signal processing and machine learning. | |||||
Inhalt | 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, singular-value decomposition and pseudo-inverse, principal-components 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. | |||||
Skript | Lecture notes. | |||||
Voraussetzungen / Besonderes | Prerequisites: - local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.) - others: solid basics in linear algebra and probability theory | |||||
227-0627-00L | Angewandte Computer Architektur | W | 6 KP | 4G | A. Gunzinger | |
Kurzbeschreibung | Diese Vorlesung gibt einen Überblick über die Anforderungen und die Architektur von parallelen Computersystemen unter Berücksichtigung von Rechenleistung, Zuverlässigkeit und Kosten. | |||||
Lernziel | Arbeitsweise von parallelen Computersystemen verstehen, solche Systeme entwerfen und modellieren. | |||||
Inhalt | Die Vorlesung Angewandte Computer Architektur gibt technische und unternehmerische Einblicke in innovative Computersysteme/Architekturen (CPU, GPU, FPGA, Spezialprozessoren) und deren praxisnahe Umsetzung. Dabei werden oft die Grenzen der technologischen Möglichkeiten ausgereizt. Wie ist das Computersystem aufgebaut, das die über 1000 Magneten an der Swiss Light Source (SLS) steuert? Wie ist das hochverfügbare Alarmzentrum der SBB aufgebaut? Welche Computer Architekturen werden in Fahrerassistenzsystemen verwendet? Welche Computerarchitektur versteckt sich hinter einem professionellen digitalen Audio Mischpult? Wie können Datenmengen von 30 TB/s, wie sie bei einem Protonen-Beschleuniger entstehen, in Echtzeit verarbeitet werden? Kann die aufwändige Berechnung der Wettervorhersage auch mit GPUs erfolgen? Nach welcher Systematik können optimale Computerarchitekturen gefunden werden? Welche Faktoren sind entscheidend, um solche Projekte erfolgreich umzusetzen? | |||||
Skript | Skript und Übungsblätter. | |||||
Voraussetzungen / Besonderes | Voraussetzungen: Grundlagen der Computerarchitektur. | |||||
252-0417-00L | Randomized Algorithms and Probabilistic Methods | W | 8 KP | 3V + 2U + 2A | A. Steger | |
Kurzbeschreibung | 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 | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Skript | Yes. | |||||
Literatur | - Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995) - Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005) | |||||
252-0206-00L | Visual Computing | W | 8 KP | 4V + 3U | M. Pollefeys, S. Coros | |
Kurzbeschreibung | 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. | |||||
Lernziel | 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. | |||||
Inhalt | 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. | |||||
Skript | 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. | |||||
Literatur | Markus Gross: Computer Graphics, scriptum, 1994-2005 | |||||
252-0546-00L | Physically-Based Simulation in Computer Graphics | W | 4 KP | 2V + 1U | M. Bächer, V. da Costa de Azevedo, B. Solenthaler | |
Kurzbeschreibung | Die Vorlesung gibt eine Einführung in das Gebiet der physikalisch basierten Animation in der Computer Graphik und einen Überblick über fundamentale Methoden und Algorithmen. In den praktischen Übungen werden drei Aufgabenblätter in kleinen Gruppen bearbeitet. Zudem sollen in einem Programmierprojekt die Vorlesungsinhalte in einem 3D Spiel oder einer vergleichbaren Anwendung umgesetzt werden. | |||||
Lernziel | Die Vorlesung gibt eine Einführung in das Gebiet der physikalisch basierten Animation in der Computer Graphik und einen Überblick über fundamentale Methoden und Algorithmen. In den praktischen Übungen werden drei Aufgabenblätter in kleinen Gruppen bearbeitet. Zudem sollen in einem Programmierprojekt die Vorlesungsinhalte in einem 3D Spiel oder einer vergleichbaren Anwendung umgesetzt werden. | |||||
Inhalt | In der Vorlesung werden Themen aus dem Gebiet der physikalisch-basierten Modellierung wie Partikel-Systeme, Feder-Masse Modelle, die Methoden der Finiten Differenzen und der Finiten Elemente behandelt. Diese Methoden und Techniken werden verwendet um deformierbare Objekte oder Flüssigkeiten zu simulieren mit Anwendungen in Animationsfilmen, 3D Computerspielen oder medizinischen Systemen. Es werden auch Themen wie Starrkörperdynamik, Kollisionsdetektion und Charakteranimation behandelt. | |||||
Voraussetzungen / Besonderes | Basiskenntnisse in Analysis und Physik, Algorithmen und Datenstrukturen und der Programmierung in C++. Kenntnisse auf den Gebieten Numerische Mathematik sowie Gewoehnliche und Partielle Differentialgleichungen sind von Vorteil, werden aber nicht vorausgesetzt. | |||||
401-3627-00L | High-Dimensional Statistics Findet dieses Semester nicht statt. | W | 4 KP | 2V | P. L. Bühlmann | |
Kurzbeschreibung | "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. | |||||
Lernziel | Knowledge of methods and basic theory for high-dimensional statistical inference | |||||
Inhalt | 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 | |||||
Literatur | 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. | |||||
Voraussetzungen / Besonderes | 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 | W | 6 KP | 3G | N. Meinshausen | |
Kurzbeschreibung | Statistical analysis and modeling of observations in temporal order, which exhibit dependence. Stationarity, trend estimation, seasonal decomposition, autocorrelations, spectral and wavelet analysis, ARIMA-, GARCH- and state space models. Implementations in the software R. | |||||
Lernziel | Understanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R. | |||||
Inhalt | This course deals with modeling and analysis of variables which change randomly in time. Their essential feature is the dependence between successive observations. Applications occur in geophysics, engineering, economics and finance. Topics covered: Stationarity, trend estimation, seasonal decomposition, autocorrelations, spectral and wavelet analysis, ARIMA-, GARCH- and state space models. The models and techniques are illustrated using the statistical software R. | |||||
Skript | Not available | |||||
Literatur | A list of references will be distributed during the course. | |||||
Voraussetzungen / Besonderes | Basic knowledge in probability and statistics | |||||
401-3901-00L | Mathematical Optimization | W | 11 KP | 4V + 2U | R. Weismantel | |
Kurzbeschreibung | Mathematical treatment of diverse optimization techniques. | |||||
Lernziel | Advanced optimization theory and algorithms. | |||||
Inhalt | 1) Linear optimization: The geometry of linear programming, the simplex method for solving linear programming problems, Farkas' Lemma and infeasibility certificates, duality theory of linear programming. 2) Nonlinear optimization: Lagrange relaxation techniques, Newton method and gradient schemes for convex optimization. 3) Integer optimization: Ties between linear and integer optimization, total unimodularity, complexity theory, cutting plane theory. 4) Combinatorial optimization: Network flow problems, structural results and algorithms for matroids, matchings, and, more generally, independence systems. | |||||
Literatur | 1) D. Bertsimas & R. Weismantel, "Optimization over Integers". Dynamic Ideas, 2005. 2) A. Schrijver, "Theory of Linear and Integer Programming". John Wiley, 1986. 3) D. Bertsimas & J.N. Tsitsiklis, "Introduction to Linear Optimization". Athena Scientific, 1997. 4) Y. Nesterov, "Introductory Lectures on Convex Optimization: a Basic Course". Kluwer Academic Publishers, 2003. 5) C.H. Papadimitriou, "Combinatorial Optimization". Prentice-Hall Inc., 1982. | |||||
Voraussetzungen / Besonderes | Linear algebra. |
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