Suchergebnis: Katalogdaten im Herbstsemester 2017

Rechnergestützte Wissenschaften Bachelor Information
Wahlfächer
NummerTitelTypECTSUmfangDozierende
151-0113-00LApplied Fluid DynamicsW4 KP2V + 1UJ.‑P. Kunsch
KurzbeschreibungAngewandte 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.
LernzielAllgemein anwendbare Methoden der Strömungslehre und der Gasdynamik sollen hier an ausgewählten, aktuellen Fallbeispielen illustriert und geübt werden.
InhaltBei 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.).
Skriptnicht verfügbar
Voraussetzungen / BesonderesVoraussetzungen: Fluiddynamik I und II, Thermodynamik I und II
151-0709-00LStochastic Methods for Engineers and Natural Scientists Belegung eingeschränkt - Details anzeigen
Number of participants limited to 30.
W4 KP3GD. W. Meyer-Massetti
KurzbeschreibungThe 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.
LernzielBy 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.
SkriptDetailed lecture notes will be provided.
LiteraturSome 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
151-0317-00LVisualization, Simulation and Interaction - Virtual Reality IIW4 KP3GA. Kunz
KurzbeschreibungThis 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.
LernzielVirtual 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.
InhaltIntroduction 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
SkriptThe handout is available in German and English.
Voraussetzungen / BesonderesPrerequisites:
"Visualization, Simulation and Interaction - Virtual Reality I" is recommended.

Didactical concept:
The course consists of lectures and exercises.
151-0833-00LPrinciples of Nonlinear Finite-Element-Methods Information W5 KP2V + 2UN. Manopulo, B. Berisha
KurzbeschreibungDie 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.
LernzielDas 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
Skriptja
LiteraturBathe, K. J., Finite-Elemente-Methoden, Springer-Verlag, 2002
Voraussetzungen / BesonderesBei einer grossen Anzahl von Studenten werden bei Bedarf zwei Übungstermine angeboten.
263-5001-00LIntroduction to Finite Elements and Sparse Linear System Solving Information W4 KP2V + 1UP. Arbenz
KurzbeschreibungThe finite element (FE) method is the method of choice for (approximately) solving partial differential equations on complicated domains. In the first third of the lecture, we give an introduction to the method. The rest of the lecture will be devoted to methods for solving the large sparse linear systems of equation that a typical for the FE method. We will consider direct and iterative methods.
LernzielStudents will know the most important direct and iterative solvers for sparse linear systems. They will be able to determine which solver to choose in particular situations.
InhaltI. THE FINITE ELEMENT METHOD

(1) Introduction, model problems.

(2) 1D problems. Piecewise polynomials in 1D.

(3) 2D problems. Triangulations. Piecewise polynomials in 2D.

(4) Variational formulations. Galerkin finite element method.

(5) Implementation aspects.


II. DIRECT SOLUTION METHODS

(6) LU and Cholesky decomposition.

(7) Sparse matrices.

(8) Fill-reducing orderings.


III. ITERATIVE SOLUTION METHODS

(9) Stationary iterative methods, preconditioning.

(10) Preconditioned conjugate gradient method (PCG).

(11) Incomplete factorization preconditioning.

(12) Multigrid preconditioning.

(13) Nonsymmetric problems (GMRES, BiCGstab).

(14) Indefinite problems (SYMMLQ, MINRES).
Literatur[1] M. G. Larson, F. Bengzon: The Finite Element Method: Theory, Implementation, and Applications. Springer, Heidelberg, 2013.

[2] H. Elman, D. Sylvester, A. Wathen: Finite elements and fast iterative solvers. OUP, Oxford, 2005.

[3] Y. Saad: Iterative methods for sparse linear systems (2nd ed.). SIAM, Philadelphia, 2003.

[4] T. Davis: Direct Methods for Sparse Linear Systems. SIAM, Philadelphia, 2006.

[5] H.R. Schwarz: Die Methode der finiten Elemente (3rd ed.). Teubner, Stuttgart, 1991.
Voraussetzungen / BesonderesPrerequisites: Linear Algebra, Analysis, Computational Science.
The exercises are made with Matlab.
263-2800-00LDesign of Parallel and High-Performance Computing Information W7 KP3V + 2U + 1AT. Hoefler, M. Püschel
KurzbeschreibungAdvanced topics in parallel / concurrent programming.
LernzielUnderstand 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.
227-0102-00LDiskrete Ereignissysteme Information W6 KP4GL. Thiele, L. Vanbever, R. Wattenhofer
KurzbeschreibungEinführung 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.
LernzielOver 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.
Inhalt1. Introduction
2. Automata and Languages
3. Smarter Automata
4. Specification Models
5. Stochastic Discrete Event Systems
6. Worst-Case Event Systems
7. Network Calculus
SkriptAvailable
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
227-0116-00LVLSI I: From Architectures to VLSI Circuits and FPGAs Information W6 KP5GF. K. Gürkaynak, L. Benini
KurzbeschreibungThis 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.
LernzielUnderstand 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.
InhaltThis 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.
SkriptTextbook and all further documents in English.
LiteraturH. Kaeslin: "Top-Down Digital VLSI Design, from Architectures to Gate-Level Circuits and FPGAs", Elsevier, 2014, ISBN 9780128007303.
Voraussetzungen / BesonderesPrerequisites:
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/
227-0148-00LVLSI III: Test and Fabrication of VLSI Circuits Information
Findet dieses Semester nicht statt.
W6 KP4GL. Benini
KurzbeschreibungIn 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.
LernzielLearn 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.
InhaltIn 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.
SkriptMain 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
http://link.springer.com/book/10.1007%2Fb117406
Voraussetzungen / BesonderesAlthough 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://iis-students.ee.ethz.ch/lectures/vlsi-iii/
227-0197-00LWearable Systems IW6 KP4GG. Tröster, U. Blanke
KurzbeschreibungKontexterkennung in mobilen Kommunikationssystemen (Mobiltelephon, Smart Watch, Wearable Computer) wird mit fortgeschrittenen Verfahren aus dem Bereich Sensor Data Fusion, Mustererkennung, Statistik, Data Mining und maschinelles Lernen erarbeitet.
Kontext umfasst das Verhalten von Personen und Gruppen, deren Aktivitäten, sowie das lokale und soziale Umfeld.
LernzielUnser 'Smart Phone' erkennt mit seinen eingebauten Sensoren und mit Daten aus der Umwelt in dem Internet (Crowd Sourcing) unseren Kontext, z.B. wo befinden wir uns, was tun wir, mit wem sind wir zusammen, wie geht es uns, was sind unsere möglichen Bedürfnisse. Basierend auf diesen Informationen kann uns das 'Smart Phone' situationsgerecht als persönlicher Assistent mit passenden Dienstleistungen verwöhnen. Die Kontexterkennung als zentrale Funktion mobiler Systeme bildet den Schwerpunkt dieser Vorlesung. Kontext umfasst das Verhalten von Personen und Gruppen, deren Aktivitäten, sowie das lokale und soziale Umfeld.

Im Datenpfad von den Sensoren über die Segmentierung, Merkmalsextraktion und Clusterbildiung bis zur Klassifikation des Kontextes werden fortgeschrittene Verfahren der Signalverarbeitung, der Mustererkennung, der Statistik und des Maschinellen Lernens exemplarisch eingesetzt. Sensordaten, die über Crowdsourcing-Methoden gewonnen sind, werden in die Analysen eingebunden. Der Validierung mit MATLAB folgen eine Implementierung und Testphase auf einem Smartphone.
InhaltUnser 'Smart Phone' erkennt mit seinen eingebauten Sensoren und mit Daten aus der Umwelt in dem Internet (Crowd Sourcing) unseren Kontext, z.B. wo befinden wir uns, was tun wir, mit wem sind wir zusammen, wie geht es uns, was sind unsere möglichen Bedürfnisse. Basierend auf diesen Informationen kann uns das 'Smart Phone' situationsgerecht als persönlicher Assistent mit passenden Dienstleistungen verwöhnen. Die Kontexterkennung als zentrale Funktion mobiler Systeme bildet den Schwerpunkt dieser Vorlesung. Kontext umfasst das Verhalten von Personen und Gruppen, deren Aktivitäten, sowie das lokale und soziale Umfeld.

In der Vorlesung werden folgende Themen behandelt:
Sensornetze, Sensordatenverarbeitung, Data Fusion, Zeitreihen (Segmentierung, Ähnlichkeitsmasse), überwachtes Lernen (LDA, Bayes Decision Theory, Entscheidungsbäume, Random Forest, kNN-Verfahren, Support Vector Machine, Adaboost, Deep Learning), Clustering (k-means, dbsan, topic models), Recommender Systems, Collaborative Filtering, Crowdsourcing.

Die Übungen orientieren sich an konkreten Problemstellungen wie Gesten- und Bewegungserkennung mit verteilten Sensoren, Detektion von Aktivitätsmuster, Benutzung 'crowd-generierter' Daten sowie Bestimmung des lokalen Umfeldes.

Präsentationen durch Doktorierende und der Besuch am Wearable Computing Lab führen ein in die aktuellen Forschungsthemen und die internationalen Forschungsprojekte.

Sprache: deutsch/englisch (abhängig von den TeilnehmerInnen)
SkriptManuskript zu allen Lektionen, Übungen mit Musterlösungen.
http://www.ife.ee.ethz.ch/education/wearable-systems-i.html
LiteraturLiteratur wird in den jeweiligen Vorlesungseinheiten benannt
Voraussetzungen / BesonderesKeine speziellen Voraussetzungen erforderlich
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, O. Göksel, E. Konukoglu
KurzbeschreibungLight and perception. Digital image formation. Image enhancement and feature extraction. Unitary transformations. Color and texture. Image segmentation and deformable shape matching. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThe first part of the course starts off from 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 it is investigated how the parameters of the electromagnetic waves are related to our perception. Also the interaction of light with matter is considered. The most important hardware components of technical vision systems, such as cameras, optical devices and illumination sources are discussed. The course then turns to the steps that are necessary to arrive at the discrete images that serve as input to algorithms. The next part describes necessary preprocessing steps of image analysis, 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 depth as two important examples. The estimation of image velocities (optical flow) will get due attention and methods for object tracking will be presented. Several techniques are discussed to extract three-dimensional information about objects and scenes. Finally, approaches for the recognition of specific objects as well as object classes will be discussed and analyzed.
SkriptCourse material Script, computer demonstrations, exercises and problem solutions
Voraussetzungen / BesonderesPrerequisites:
Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on Linux and C.
The course language is English.
227-0417-00LInformation Theory IW6 KP4GA. Lapidoth
KurzbeschreibungThis 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.
LernzielThe fundamentals of Information Theory including Shannon's source coding and channel coding theorems
InhaltThe 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
LiteraturT.M. Cover and J. Thomas, Elements of Information Theory (second edition)
227-0427-00LSignal and Information Processing: Modeling, Filtering, LearningW6 KP4GH.‑A. Loeliger
KurzbeschreibungFundamentals in signal processing, detection/estimation, and machine learning.
I. Linear signal representation and approximation: Hilbert spaces, LMMSE estimation, regularization and sparsity.
II. Learning linear and nonlinear functions and filters: kernel methods, neural networks.
III. Structured statistical models: hidden Markov models, factor graphs, Kalman filter, parameter estimation.
LernzielThe course is an introduction to some basic topics in signal processing, detection/estimation theory, and machine learning.
InhaltPart 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, sparse Bayesian learning.
SkriptLecture notes.
Voraussetzungen / BesonderesPrerequisites:
- local bachelors: course "Discrete-Time and Statistical Signal Processing" (5. Sem.)
- others: solid basics in linear algebra and probability theory
227-0627-00LAngewandte Computer ArchitekturW6 KP4GA. Gunzinger
KurzbeschreibungDiese Vorlesung gibt einen Überblick über die Anforderungen und die Architektur von parallelen Computersystemen unter Berücksichtigung von Rechenleistung, Zuverlässigkeit und Kosten.
LernzielArbeitsweise von parallelen Computersystemen verstehen, solche Systeme entwerfen und modellieren.
InhaltDie 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?
SkriptSkript und Übungsblätter.
Voraussetzungen / BesonderesVoraussetzungen:
Grundlagen der Computerarchitektur.
252-0417-00LRandomized Algorithms and Probabilistic MethodsW8 KP3V + 2U + 2AA. Steger, E. Welzl
KurzbeschreibungLas 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
LernzielAfter 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.
InhaltRandomized 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.
SkriptYes.
Literatur- Randomized Algorithms, Rajeev Motwani and Prabhakar Raghavan, Cambridge University Press (1995)
- Probability and Computing, Michael Mitzenmacher and Eli Upfal, Cambridge University Press (2005)
252-0206-00LVisual Computing Information W8 KP4V + 3US. Coros, O. Hilliges
KurzbeschreibungThis 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.
LernzielThis 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.
InhaltCourse 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.
SkriptA 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.
LiteraturMarkus Gross: Computer Graphics, scriptum, 1994-2005
252-0546-00LPhysically-Based Simulation in Computer Graphics Information W4 KP2V + 1UM. Bächer, V. da Costa de Azevedo
KurzbeschreibungDie 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.
LernzielDie 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.
InhaltIn 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 / BesonderesBasiskenntnisse 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.
401-3627-00LHigh-Dimensional Statistics
Findet dieses Semester nicht statt.
W4 KP2VP. 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.
LernzielKnowledge of methods and basic theory for high-dimensional statistical inference
InhaltLasso 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
LiteraturPeter 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 / BesonderesKnowledge of basic concepts in probability theory, and intermediate knowledge of statistics (e.g. a course in linear models or computational statistics).
401-4623-00LTime Series Analysis
Findet dieses Semester nicht statt.
W6 KP3Gkeine Angaben
KurzbeschreibungStatistical 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.
LernzielUnderstanding of the basic models and techniques used in time series analysis and their implementation in the statistical software R.
InhaltThis 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.
SkriptNot available
LiteraturA list of references will be distributed during the course.
Voraussetzungen / BesonderesBasic knowledge in probability and statistics
401-3901-00LMathematical Optimization Information W11 KP4V + 2UR. Weismantel
KurzbeschreibungMathematical treatment of diverse optimization techniques.
LernzielAdvanced optimization theory and algorithms.
Inhalt1) 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.
Literatur1) 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 / BesonderesLinear algebra.
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