Ab 2. November 2020 findet das Herbstsemester 2020 online statt. Ausnahmen: Veranstaltungen, die nur mit Präsenz vor Ort durchführbar sind.
Bitte beachten Sie die per E-Mail kommunizierten Informationen der Dozierenden.

Suchergebnis: Katalogdaten im Herbstsemester 2018

Rechnergestützte Wissenschaften Master Information
Wahlfächer
Von den angebotenen Wahlfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden.
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 45.
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
151-0532-00LNonlinear Dynamics and Chaos I Information W4 KP2V + 2UF. Kogelbauer
KurzbeschreibungBasic facts about nonlinear systems; stability and near-equilibrium dynamics; bifurcations; dynamical systems on the plane; non-autonomous dynamical systems; chaotic dynamics.
LernzielThis course is intended for Masters and Ph.D. students in engineering sciences, physics and applied mathematics who are interested in the behavior of nonlinear dynamical systems. It offers an introduction to the qualitative study of nonlinear physical phenomena modeled by differential equations or discrete maps. We discuss applications in classical mechanics, electrical engineering, fluid mechanics, and biology. A more advanced Part II of this class is offered every other year.
Inhalt(1) Basic facts about nonlinear systems: Existence, uniqueness, and dependence on initial data.

(2) Near equilibrium dynamics: Linear and Lyapunov stability

(3) Bifurcations of equilibria: Center manifolds, normal forms, and elementary bifurcations

(4) Nonlinear dynamical systems on the plane: Phase plane techniques, limit sets, and limit cycles.

(5) Time-dependent dynamical systems: Floquet theory, Poincare maps, averaging methods, resonance
SkriptThe class lecture notes will be posted electronically after each lecture. Students should not rely on these but prepare their own notes during the lecture.
Voraussetzungen / Besonderes- Prerequisites: Analysis, linear algebra and a basic course in differential equations.

- Exam: two-hour written exam in English.

- Homework: A homework assignment will be due roughly every other week. Hints to solutions will be posted after the homework due dates.
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-MethodsW5 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.
263-2800-00LDesign 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 W6 KP4GF. K. Gürkaynak, L. 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/
227-0447-00LImage 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. Motion extraction and tracking. 3D data extraction. Invariant features. Specific object recognition and object class recognition. Deep learning and Convolutional Neural Networks.
LernzielOverview of the most important concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
InhaltThis 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.
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 Python and Linux.
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 Analysis, Models, and Machine LearningW6 KP4GH.‑A. Loeliger
KurzbeschreibungMathematical 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.
LernzielThe course is an introduction to some basic topics in signal processing 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, linear Gaussian models with sparse events.
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.
252-0237-00LConcepts of Object-Oriented Programming Information W6 KP3V + 2UP. Müller
KurzbeschreibungCourse that focuses on an in-depth understanding of object-oriented programming and compares designs of object-oriented programming languages. Topics include different flavors of type systems, inheritance models, encapsulation in the presence of aliasing, object and class initialization, program correctness, reflection
LernzielAfter this course, students will:
Have a deep understanding of advanced concepts of object-oriented programming and their support through various language features. Be able to understand language concepts on a semantic level and be able to compare and evaluate language designs.
Be able to learn new languages more rapidly.
Be aware of many subtle problems of object-oriented programming and know how to avoid them.
InhaltThe main goal of this course is to convey a deep understanding of the key concepts of sequential object-oriented programming and their support in different programming languages. This is achieved by studying how important challenges are addressed through language features and programming idioms. In particular, the course discusses alternative language designs by contrasting solutions in languages such as C++, C#, Eiffel, Java, Python, and Scala. The course also introduces novel ideas from research languages that may influence the design of future mainstream languages.

The topics discussed in the course include among others:
The pros and cons of different flavors of type systems (for instance, static vs. dynamic typing, nominal vs. structural, syntactic vs. behavioral typing)
The key problems of single and multiple inheritance and how different languages address them
Generic type systems, in particular, Java generics, C# generics, and C++ templates
The situations in which object-oriented programming does not provide encapsulation, and how to avoid them
The pitfalls of object initialization, exemplified by a research type system that prevents null pointer dereferencing
How to maintain the consistency of data structures
LiteraturWill be announced in the lecture.
Voraussetzungen / BesonderesPrerequisites:
Mastering at least one object-oriented programming language (this course will NOT provide an introduction to object-oriented programming); programming experience
252-0417-00LRandomized Algorithms and Probabilistic MethodsW8 KP3V + 2U + 2AA. Steger
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)
252-0546-00LPhysically-Based Simulation in Computer Graphics Information W4 KP2V + 1UM. Bächer, V. da Costa de Azevedo, B. Solenthaler
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.
261-5100-00LComputational Biomedicine Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 60.
W4 KP2V + 1UG. Rätsch
KurzbeschreibungThe course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
LernzielOver the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.
InhaltThe course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine:
1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches.
2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes.
3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.
Voraussetzungen / BesonderesData Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line
401-4503-68LReading Course: Reduced Basis MethodsW4 KP2GR. Hiptmair
KurzbeschreibungReduced Basis Methods (RBM) allow the efficient repeated numerical soluton of parameter depedent differential equations, which arise, e.g., in PDE-constrained optimization, optimal control, inverse problems, and uncertainty quantification. This course introduces the mathematical foundations of RBM and discusses algorithmic and implementation aspects.
Lernziel* Knowledge about the main principles underlying RBMs
* Familiarity with algorithms for the construction of reduced bases
* Knowledge about the role of and techniques for a posteriori error estimation.
* Familiarity with some applications of RBMs.
LiteraturMain reference:
Hesthaven, Jan S.; Rozza, Gianluigi; Stamm, Benjamin, Certified reduced basis methods for parametrized partial differential equations. SpringerBriefs in Mathematics, 2016

Supplementary reference:
Quarteroni, Alfio; Manzoni, Andrea; Negri, Federico, Reduced basis methods for partial differential equations. An introduction. Unitext 92, Springer, Cham, 2016.
Voraussetzungen / BesonderesThis is a reading course, which will closely follow the book by J. Hesthaven, G. Rozza and B. Stamm. Participants are expected to study particular sections of the book every week, which will then be discussed during the course sessions.
401-4619-67LAdvanced Topics in Computational Statistics
Findet dieses Semester nicht statt.
W4 KP2VN. Meinshausen
KurzbeschreibungThis lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling.
LernzielStudents learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.
InhaltThe main focus will be on graphical models in various forms:
Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models
Voraussetzungen / BesonderesWe assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.
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).
  •  Seite  1  von  2 Nächste Seite Letzte Seite     Alle