Search result: Catalogue data in Spring Semester 2009

Number	Title	Type	ECTS	Hours	Lecturers
Electrical Engineering and Information Technology Master
Courses of the specialisation
Communication A total of 42 CP must be achieved form courses during the Master Program. The individual study plan is subject to the tutor's approval.
Core Subjects These core subjects are particularly recommended for the field of "Communications".
227-0436-00L	Digital Communication and Signal Processing	W	6 credits	2V + 2U	A. Wittneben
Abstract	A unified presentation of modern modulation, detection and synchronization schemes and relevant aspects of signal processing enables the student to analyze, simulate, implement and research the physical layer of advanced digital communication schemes.
Objective	Digital communication systems are characterized by ever increasing requirements on data rate, spectral efficiency and reliability. Due to the huge advances in very large scale integration (VLSI) we are now able to implement extremely complex digital signal processing algorithms to meet these challenges. As a result the physical layer (PHY) of digital communication systems has become the dominant function in most state-of-the-art system designs. In this course we discuss the major elements of PHY implementations in a rigorous theoretical fashion and present important practical examples to illustrate the application of the theory. In Part I we treat discrete time linear adaptive filters, which are a core component to handle multiuser and intersymbol interference in time-variant channels. In Part II we develop a theoretical framework for multidimensional modulation and detection. Our approach covers all state-of-the-art linear modulation schemes and includes multiuser and MIMO wireless scenarios in a natural fashion. Special emphasis is on lossless discrete system representations, which are fundamental for digital signal processing. In Part III we cover parameter estimation and synchronization. Based on the classical discrete detection and estimation theory we develop digital algorithms for symbol timing and frequency synchronization.
Content	Part I: Linear adaptive filters for digital communication • Finite impulse response (FIR) filter for temporal and spectral shaping • Wiener filters • Method of steepest descent • Least mean square adaptive filters Part II: A theoretical framework for multi-dimensional modulation and detection • Modulation theory • Linear modulation schemes • Optimum receiver and discrete system models Part III: Parameter estimation and synchronization • Discrete detection theory • Discrete estimation theory • Synthesis of synchronization algorithms
Lecture notes	Lecture notes.
Literature	[1] Oppenheim, A. V., Schafer, R. W., "Discrete-time signal processing", Prentice-Hall, ISBN 0-13-754920-2. [2] Haykin, S., "Adaptive filter theory", Prentice-Hall, ISBN 0-13-090126-1. [3] Van Trees, H. L., "Detection , estimation and modulation theory", John Wiley&Sons, ISBN 0-471-09517-6. [4] Meyr, H., Moeneclaey, M., Fechtel, S. A., "Digital communication receivers: synchronization, channel estimation and signal processing", John Wiley&Sons, ISBN 0-471-50275-8.
Prerequisites / Notice	Prerequisites: Communication Systems.
227-0438-00L	Fundamentals of Wireless Communication	W	6 credits	2V + 2U	H. Bölcskei
Abstract	The class focuses on fundamental communication-theoretic aspects of modern wireless communication systems. Main topics covered are the system-theoretic characterization of wireless channels, the principle of diversity and various diversity techniques, and information theoretic aspects of communication over fading channels like the notions of ergodic and outage capacity.
Objective	After attending this lecture, participating in the discussion sections and working on the homework problem sets, students should be able to - understand the nature of the fading mobile radio channel and its implications for the design of communication systems - analyze existing communication systems - apply the fundamental principles to new wireless communication systems, especially in the design of diversity techniques and coding schemes
Content	Wireless communications, elements of information theory, wireless channels (fading channels), ergodic and outage capacity of fading channels, coded modulation for fading channels, adaptive modulation, interleaving, error probability, error exponent, cutoff rate, multiple antenna (MIMO) systems, space-time codes, Orthogonal frequency division multiplexing (OFDM), fading multi-access and broadcast channels.
Lecture notes	A draft version of the lecture notes is available and will be handed out during the lectures
Literature	A set of handouts covering digital communication basics and mathematical preliminaries is available on the website. For further reading, we recommend - J. M. Wozencraft and I.M. Jacobs, "Principles of Communication Engineering," Wiley, 1965 - A. Papoulis and S.U. Pillai, "Probability, Random Variables and Stochastic Processes," McGraw Hill, 4th edition, 2002 - G. Strang, Linear Algebra and its Applications," Harcourt, 3rd edition, 1988 - T.M. Cover and J.A. Thomas, Elements of Information Theory," Wiley, 1991
Prerequisites / Notice	This class will be taught in English. The oral exam will be in German (unless you wish to take it in English, of course). A prerequisite for this course is a working knowledge in digital communications, random processes and detection theory.
227-0418-00L	Algebra and Error Correcting Codes	W	6 credits	4G	H.‑A. Loeliger
Abstract	The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course is also an introduction to "abstract" algebra and some of its applications in coding and signal processing.
Objective	The course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course is also an introduction to "abstract" algebra and some of its applications in coding and signal processing.
Content	Coding: coding and modulation, linear codes, Hamming space codes, Euclidean space codes, trellises and Viterbi decoding, convolutional codes, factor graphs and message passing algorithms, low-density parity check codes, turbo codes, Reed-Solomon codes. Algebra: groups, rings, homomorphisms, ideals, fields, finite fields, vector spaces, polynomials, Chinese Remainder Theorem.
Lecture notes	Lecture Notes (english)
227-0166-00L	Analog Integrated Circuits	W	6 credits	4G	T. Burger
Abstract	This course provides a foundation in analog integrated circuit design based on bipolar and CMOS technology.
Objective	The student understands the basic elements, design issues and techniques for analog integrated circuit design. He/she is able to analyse and design basic circuits such as bias networks, amplification stages and amplifiers and to determine the parameters that govern their performance.
Content	Review of bipolar and MOS devices and their small-signal equivalent circuit models; Building blocks in analog circuits such as current sources, active load, current mir- rors, supply independent biasing etc; Amplifiers: differential amplifiers, cascode amplifier, high gain structures, output stages, gain bandwidth product of op-amps; Stability; Comparators; Second-order effects in analog circuits such as mismatch, noise and offset; A/D and D/A converters; Analog multipliers; Introduction to switched capacitor circuits; Oscillators. The exercise sessions aim to reinforce the lecture material by well guided step-by-step design tasks. The circuit simulator SPECTRE is used to facilitate the tasks. There is also an experimental session on op-amp measurments.
Lecture notes	Handouts of presented slides, no script.
Literature	Gray & Meyer, Analysis and Design of Analog Integrated Circuits, 4th Ed. Wiley, '01.
227-0558-00L	Principles of Distributed Computing	W	6 credits	2V + 2U	R. Wattenhofer
Abstract	We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.
Objective	In the last two decades, we have experienced an unprecedented growth in the area of distributed systems and networks; distributed computing now encompasses many of the activities occurring in today's computer and communications world. This course introduces the principles of distributed computing, highlighting common themes and techniques. We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the “pearls” of distributed computing. We will cover a fresh topic every week.
Content	• distributed computing models, e.g. message passing, shared memory, multi-core, synchronous and asynchronous systems, altruistic/selfish/faulty/malicious behavior • distributed network algorithms such as leader election, coloring, covering, packing, decomposition, spanning tree computation, and lower bounds • multi-core and shared memory algorithms such as agreement or snapshot, shared objects and variables • peer-to-peer systems, small-world networks, sorting networks, self-organizing systems
Lecture notes	Available
Literature	Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8
Prerequisites / Notice	Course pre-requisites: Interest in algorithmic problems. (No particular course needed.)

Recommended Subjects These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.
Number	Title	Type	ECTS	Hours	Lecturers
227-0434-00L	Harmonic Analysis: Theory and Applications in Advanced Signal Processing	W	6 credits	2V + 2U	H. Bölcskei
Abstract	Introduction to basic concepts in harmonic analysis with applications in signal processing and information theory.
Objective	Introduction to basic concepts in harmonic analysis with applications in signal processing and information theory.
Content	Elements of linear algebra, Fourier theory and sampling, Hilbert spaces, linear operator theory, frame theory, approximation theory, wavelets, short-time Fourier transform, Gabor expansion, filter banks, transform coding, sparse signals, uncertainty principles, compressed sensing.
Lecture notes	Lecture notes, problem sets with documented solutions.
Literature	S. Mallat, "A wavelet tour of signal processing", 2n ed., Academic Press, 1999 M. Vetterli and J. Kovacevic, "Wavelets and subband coding", Prentice Hall, 1995 I. Daubechies, "Ten lectures on wavelets", SIAM, 1992 O. Christensen, "An introduction to frames and Riesz bases", Birkhäuser, 2003 M. A. Pinksy, "Introduction to Fourier analysis and wavelets", Brooks/ Cole Series in Advanced Mathematics, 2002.
227-0468-00L	Analog Signal Processing and Filtering	W	6 credits	2V + 2U	H. Schmid
Abstract	This lecture provides a wide overview over analogue (integreted) filters (continuous-time and discrete-time), amplifiers, and sigma-delta converters, by treating all these circuits using signal-flow considerations. The lecture is suitable for both analog and digital designers. The exam allows for the different interests of the two groups.
Objective	This lecture provides a wide overview over analogue (integreted) filters (continuous-time and discrete-time), amplifiers, and sigma-delta converters, by treating all these circuits using signal-flow considerations. The lecture is suitable for both analog and digital designers. The exam allows for the different interests of the two groups. The learning goal is that the students understand the signal flow in such circuits and also non-ideal effects well enough to enable them to gain an understanding of further circuits by themselves.
Content	The theory and CMOS implementation of active Filters is discussed in detail using the example of Gm-C filters. A 1xDVD read channel filter is designed in a computer exercise using Cadence design tools. The theory of active filters is taken up again by discussing single-amplifier biquadratic filters. Theory and implementation of opamps, current conveyors, and inductor simulators follow. Finally, an introduction to switched-capacitor filters and circuits is given, including sensor read-out amplifiers, correlated double sampling, and chopping. These topics form the basis for the longest part of the lecture: the discussion of sigma-delta A/D and D/A converters, which are portrayed as mixed analog-digital (MAD) filters in this lecture.
Lecture notes	The base for these lectures are two or three published scientific papers. From these papers we will together develop the technical content. Support material can be found in the book "Analog Integrated Circuit Design", David Johns und Ken Martin, John Wiley & Sons, 1997. This book is offered in the lecture at the student price, which is much lower than the market price. Further course material is provided free of charge.
Literature	Contents and Material of the 2008 lecture series: Link (The contents change slightly each year.)
Prerequisites / Notice	Prerequisites: Recommended (but not required): Stochastic models and signal processing, Communication Electronics, Analog Integrated Circuits, Transmission Lines and Filters.
227-0448-00L	Image Analysis and Computer Vision II 4 credit points under the programme regulations 2001	W	6 credits	4G	V. Ferrari, L. Van Gool
Abstract	Introduction into the basic procedures for the interpretation of image content and object recognition. Demonstrating the current capabilities of computer vision systems through selected applications. Gaining own experience through practical computer and programming exercises.
Objective	Overview of the basic concepts of image formation, perception and analysis, and Computer Vision. Gaining own experience through practical computer and programming exercises.
Content	Basics of visual perception. Usage of unitary transforms, Principal and Independent Component Analysis for representing image information. Colour perception and representation. Object description based on surface features. Texture characterization and analysis, including stochastic methods. Deformable contour models, snakes and thin plate splines. Tracking based on local features. Particle filters. Shape characterization using invariant descriptors, geometric invariants. Combination of shape and surface features using moment invariants. Object recognition for specific objects and object classes, image and model based schemes.
Lecture notes	Course material Script, computer demonstrations, exercises and problem solutions.
Prerequisites / Notice	Prerequisites: Bildatenanalyse und Computer Vision I. Basic concepts of mathematical analysis and linear algebra. The computer exercises are based on UNIX and C. The course will be held in English.
227-0478-00L	Acoustics II	W	6 credits	4G	K. Heutschi
Abstract	Advanced knowledge of the functioning and application of electro-acoustic transducers.
Objective	Advanced knowledge of the functioning and application of electro-acoustic transducers
Content	Electrical, mechanical and acoustical analogies. Transducers, microphones and loudspeakers, acoustics of musical instruments, sound recording, sound reproduction, digital audio.
Lecture notes	available
227-0148-00L	VLSI III: Test and Fabrication of VLSI Circuits	W	6 credits	4G	W. Fichtner, N. Felber, H. Kaeslin
Abstract	Know how to apply methods, software tools and equipment for designing testable VLSI circuits, for testing fabricated ICs, and for physical analysis in the occurrence of defective parts. A basic understanding of modern semiconductor technologies. Being familiar with decision criteria of economic nature and with models of industrial cooperation.
Objective	Know how to apply methods, software tools and equipment for designing testable VLSI circuits, for testing fabricated ICs, and for physical analysis in the occurrence of defective parts. A basic understanding of modern semiconductor technologies. Being familiar with decision criteria of economic nature and with models of industrial cooperation.
Content	This final course in a series of three focusses on manufacturing, testing, physical analysis, and packaging of VLSI circuits. Topics include: Effects of fabrication defects, abstraction from physical to transistor- and gate-level fault models, fault grading of large ASICs, generation of efficient test vector sets, enhancement of testability with built-in self test, organisation and application of automated test equipment, physical analysis of devices, packaging problems and solutions. The course further addresses: Models of industrial cooperation, the caveats of virtual components, the cost structures of ASIC development and manufacturing, market requirements, decision criteria, and case studies. Today's deep-submicron CMOS fabrication processes, outlook on the future evolution of semiconductor technology. Exercises teach students how to use CAE/CAD software and automated equipment for testing ASICs after fabrication. Students that have submitted a design for manufacturing at the end of the 7th term do so on their own circuits. Physical analysis methods with professional equipment (AFM, DLTS) complement this training.
Lecture notes	English lecture notes (Dr. N. Felber).
Literature	"Digital Integrated Circuit Design, from VLSI Architectures to CMOS Fabrication" Cambridge University Press, 2008, ISBN 9780521882675 (Dr. H. Kaeslin).
Prerequisites / Notice	Prerequisites: Basic knowledge of digital design.
227-0456-00L	High Frequency and Microwave Electronics I Does not take place this semester.	W	6 credits	4G	C. Bolognesi
Abstract	Understanding of basic building blocks of microwave electronics technology, with a focus on active semiconductor devices.
Objective	Understanding the fundamentals of microwave electronics technology, with emphasis on active components.
Content	Introduction, microstrip transmission lines, matching, semiconductors, pn-junction, noise, PIN-diode and applications, Schottky diodes and detectors, bipolar transistors and heterojunction bipolar transistors, MESFET physics and properties, high-electron mobility transistors, microwave amplifiers.
Lecture notes	Script: Mikrowellentechnik and Mikrowellenelektronik, by Werner Bächtold (In German).
Prerequisites / Notice	The lectures will be held in English.
263-9000-00L	Information Processing with Neural Networks	W	4 credits	2V + 1U	J. Bernasconi
Abstract	Information processing with artificial neural networks (Basic principles and applications)
Objective	The course gives an introduction to the different methods and techniques of information processing with artificial neural networks. Its aim is to provide the necessary background for an efficient use of these new information processing techniques.
Content	Artificial neurons, different types of neural network paradigms (feedforward networks, Hopfield networks, winner-take-all networks), learning procedures (error backpropagation, stochastic learning, reinforcement learning, competitive learning), analysis and optimization of learning and generalization behavior, discussion and analysis of applications.
Lecture notes	Course script (including a list of further references).
Literature	Course script (with additional literature references)
227-0678-00L	Speech Processing II	W	6 credits	4G	B. Pfister
Abstract	Interdisciplinary approaches to text-to-speech synthesis and speech recognition (continuation of course speech processing I).
Objective	In this course selected concepts and interdisciplinary approaches to text-to-speech synthesis and speech recognition are presented.
Content	Fundamentals of representation and application of linguistic knowledge: Introduction of the theory of formal languages, the Chomsky hierarchy, word analysis, finite state machines, parsing. Speech synthesis: Natural language analysis (for words and sentences), lexicon, grammar for natural language; generation of the abstract representation of pronunciation (phone sequence, accents, phrases). Additionally, the ETH text-to-speech system SVOX is discussed. Speech recognition: The statistical approach to speech recognition with hidden Markov models is detailed: Basic algorithms (forward, Viterbi and Baum-Welch algorithm), problems of implementation, HMM training, whole vs. subword modeling, isolated word recognition, continuous speech recognition, statistical and rule-based language models.
Lecture notes	The following textbook will be used: "Sprachverarbeitung - Grundlagen und Methoden der Sprachsynthese und Spracherkennung", B. Pfister und T. Kaufmann, Springer Verlag, ISBN: 978-3-540-75909-6
Prerequisites / Notice	Prerequisites: Speech Processing I.
402-0804-00L	Neuromorphic Engineering II	W	6 credits	5G	T. Delbrück, R. J. Douglas, G. Indiveri, S.‑C. Liu
Abstract	This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the fall semester course "Neuromorphic Engineering I".
Objective	Design of a neuromorphic circuit for implementation with CMOS technology.
Content	This course teaches the basics of analog chip design and layout with an emphasis on neuromorphic circuits, which are introduced in the autumn semester course "Neuromorphic Engineering I". The principles of CMOS processing technology are presented. Using a set of inexpensive software tools for simulation, layout and verification, suitable for neuromorphic circuits, participants learn to simulate circuits on the transistor level and to make their layouts on the mask level. Important issues in the layout of neuromorphic circuits will be explained and illustrated with examples. In the latter part of the semester students simulate and layout a neuromorphic chip. Schematics of basic building blocks will be provided. The layout will then be fabricated and will be tested by students during the following fall semester.
Literature	S.-C. Liu et al.: Analog VLSI Circuits and Principles; software documentation.
Prerequisites / Notice	Prerequisites: Neuromorphic Engineering I strongly recommended
227-0366-00L	Introduction to Computational Electromagnetics	W	6 credits	4G	C. Hafner, R. Vahldieck
Abstract	An overview over the most prominent methods for the simulation of electromagnetic fields is given This includes domain methods such as finite differences and finite elements, method of moments, and boundary methods. Both time domain and frequency domain techniques are considered.
Objective	Overview of numerical methods for the simulation of electromagnetic fields and hands-on experiments with selected methods.
Content	Overview of concepts of the main numerical methods for the simulation of electromagnetic fields: Finite Difference Method, Finite Element Method, Transmission Line Matrix Method, Matrix Methods, Multipole Methods, Image Methods, Method of Moments, Integral Equation Methods, Beam Propagation Method, Mode Matching Technique, Spectral Domain Analysis, Method of Lines. Applications: Problems in electrostatic and magnetostatic, guided waves and free-space propagation problems, antennas, resonators, inhomogeneous transmissionlLines, nanotechnic, optics etc.
Lecture notes	Download from: Link
Prerequisites / Notice	First half of the semester: lectures; second half of the semester: exercises in form of small projects
251-0834-00L	Information Systems for Engineers	W	4 credits	2V + 1U	R. Marti
Abstract	Foundations and concepts of information systems from a user's viewpoint. The focus is on structured data: relational databases, the query language SQL, and designing relational data structures. Additional topics: search in document collections and in the Web (Information Retrieval) by estimating relevance and importance of documents with respect to a free-text query; data exchange using XML.
Objective	Following the course should enable students to 1. answer non-trivial queries on existing relational databases by formulating (entry-level) SQL statements, as well as to add new database content and to update or delete existing content, 2. formalize real-world facts in the form of a so-called entiity-relationship data model and implement this data model in the form of normalized relations (tables) 3. explain how a database management system (DBMS) essentially works and what kind of services it provides 4. explain how a search engine such as Google basically works
Content	Die Lehrveranstaltung vermittelt Grundlagen und Konzepte von Informationssystemen aus der Sicht eines Anwenders. Im Zentrum stehen relationale Datenbanksysteme, die Abfrage- und Datenmanipulationssprache SQL, sowie der Entwurf bzw. die Strukturierung relationaler Datenbanken. Dieser Stoff wird auch in praktischen Übungen vertieft. Weitere Themen sind der Umgang mit unstrukturierten und semistrukturierten Daten, die Integration von Daten aus verschiedenen autonomen Informationssystemen, sowie eine Übersicht der Architektur von Datenbanksystemen. Inhalt: 1. Einleitung. 2. Das Relationenmodell. 3. Die Abfrage- und Datenmanipulationssprache SQL. 4. Entwurf relationaler Datenbanken mit Hilfe von Entity-Relationship Diagrammen. Grundideen der Normalisierung von Relationen. 5. Architektur relationaler Datenbanksysteme. 6. Information Retrieval: Suche von (Text-) Dokumenten. Indexing, Stopwort-Elimination und Stemming. Boole'sches Retrieval und das Verktorraum-Modell. 7. Web Information Retrieval: Web-Crawling. Ausnutzen der Web-Links zwischen Web-Seiten (Page Ranking). Das Zusammenspiel von Crawling, klassischem Information Retrieval und Page Ranking. 8. Modellierung semi-strukturierter Daten mit XML und einfache Anfragen mit XPath und XQuery. 9. Zugriff auf SQL-Datenbanken aus Programmen, Transaktionen.
Literature	Vorlesungsunterlagen (PowerPoint Folien, teilweise auch zusätzlicher Text) werden auf der Web-Site publiziert. Der Kauf eines Buches wird nicht vorausgesetzt. Das Buch "Informationssysteme und Datenbanken, 7. Auflage" von C.A. Zehnder, erschienen im vdf-Verlag/Teubner-Verlag, 2002, umfasst in etwa den gleichen Stoff. Die Vorlesung ist aber nicht auf das Buch abgestimmt. Als weiterführende Literatur kann z.B. folgendes Standardwerk (ca. 1150 Seiten!) empfohlen werden: A. Silberschatz, H.F. Korth, S. Sudarshan: Database System Concepts, 5th Edition, McGraw-Hill, 2006.
Prerequisites / Notice	Voraussetzung: Elementare Kenntnisse von Mengenlehre und logischen Ausdrücken. Kenntnisse und minimale Programmiererfahrung in einer Programmiersprache wie z.B. Pascal, C, oder Java.
251-0526-00L	Statistical Learning Theory	W	5 credits	2V + 1U	J. M. Buhmann
Abstract	The course covers advanced methods of statistical learning : PAC learning and statistical learning theory;variational methods and optimization, e.g., maximum entropy techniques, information bottleneck, deterministic and simulated annealing; clustering for vectorial, histogram and relational data; model selection; graphical models.
Objective	The course surveys recent methods of statistical learning. The fundamentals of machine learning as presented in the course "Introduction to Machine Learning" are expanded and in particular, the theory of statistical learning is discussed.
Content	# Boosting: A state-of-the-art classification approach that is sometimes used as an alternative to SVMs in non-linear classification. # Theory of estimators: How can we measure the quality of a statistical estimator? We already discussed bias and variance of estimators very briefly, but the interesting part is yet to come. # Statistical learning theory: How can we measure the quality of a classifier? Can we give any guarantees for the prediction error? # Variational methods and optimization: We consider optimization approaches for problems where the optimizer is a probability distribution. Concepts we will discuss in this context include: * Maximum Entropy * Information Bottleneck * Deterministic Annealing # Clustering: The problem of sorting data into groups without using training samples. This requires a definition of ``similarity'' between data points and adequate optimization procedures. # Model selection: We have already discussed how to fit a model to a data set in ML I, which usually involved adjusting model parameters for a given type of model. Model selection refers to the question of how complex the chosen model should be. As we already know, simple and complex models both have advantages and drawbacks alike. # Reinforcement learning: The problem of learning through interaction with an environment which changes. To achieve optimal behavior, we have to base decisions not only on the current state of the environment, but also on how we expect it to develop in the future.
Lecture notes	no script; transparencies of the lectures will be made available.
Literature	Duda, Hart, Stork: Pattern Classification, Wiley Interscience, 2000. Hastie, Tibshirani, Friedman: The Elements of Statistical Learning, Springer, 2001. L. Devroye, L. Gyorfi, and G. Lugosi: A probabilistic theory of pattern recognition. Springer, New York, 1996
Prerequisites / Notice	Requirements: basic knowledge of statistics, interest in statistical methods. It is recommended that Introduction to Machine Learning (ML I) is taken first; but with a little extra effort Advanced Topics in Machine Learning can be followed without the introductory course.
251-0222-00L	Compiler Design I	W	6 credits	2V + 2U	T. Gross, F. T. Schneider
Abstract	This course uses compilers as example to expose modern software development techniques. Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: conditionals, loops, procedure calls, simple register allocation techniques.
Objective	Learn principles of compiler design, gain practical experience designing and implementing a medium-scale software system.
Content	This course uses compilers as example to expose modern software development techniques. The course introduces the students to the fundamentals of compiler construction. Students will implement a simple yet complete compiler for an object-oriented programming language for a realistic target machine. Students will learn the use of appropriate tools (parser generators); the implementation language is Java. Throughout the course, students learn to apply their knowledge of theory (automata, grammars, stack machines, program transformation) and well-known programming techniques (module definitions, design patterns, frameworks, software reuse) in a software project. Specific topics: Compiler organization. Lexical analysis. Top-down parsing via recursive descent, table-driven parsers, bottom-up parsing. Symboltables, semantic checking. Code generation for a simple RISC machine: expression evaluation, straight line code, conditionals, loops, procedure calls, simple register allocation techniques. Storage allocation on the stack, parameter passing, runtime storage management, heaps. Special topics as time permits: introduction to global dataflow and its application to register allocation, instruction scheduling.
Literature	Aho/Lam/Sethi/Ullmann, Compilers - Principles, Techniques, and Tools (2nd Edition) Muchnick, Advanced Compiler Design and Implementation, Morgan Kaufmann Publishers, 1997
Prerequisites / Notice	Prerequisites: Prior exposure to modern techniques for program construction, knowledge of at least one processor architecture at the assembly language level.
251-0532-00L	Bio-Inspired Optimization and Design	W	5 credits	2V + 1U	E. Zitzler, P. Koumoutsakos
Abstract	This lecture focuses on the foundations of bio-inspired optimization. The exercises will be oriented towards the implementation of these concepts to design applications.
Objective	You are familiar with the foundations of optimization and with different randomized search algorithms, in particular bio-inspired ones. You will be able to design, implement, and tune basic and advanced bio-inspired optimization techniques for tackling complex, large-scale applications. You will be able to evaluate different search algorithms and implementations. You are aware of the theoretical foundations of bio-inspired optimization, know the limitations as well as potential advantages and disadvantages of specific design concepts.
Content	Biologically-inspired computation is an umbrella term for different computational approaches that are based on principles or models of biological systems. This class of methods such as evolutionary algorithms, ant colony optimization, and swarm intelligence complements traditional techniques in the sense that the former can be applied to large-scale applications where little is known about the underlying problem and where the latter approaches encounter difficulties. Therefore, bio-inspired methods are becoming increasingly important in face of the complexity of today's demanding applications, and accordingly they have been successfully used in various fields ranging from computer engineering and mechanical engineering to chemical engineering and molecular biology. This lecture focuses on the foundations of bio-inspired computation with an emphasis on their application to optimization. The exercises will be oriented towards the implementation of these concepts to realistic applications.
Lecture notes	Lecture notes will be provided in the course of the semester.
227-0216-00L	Control Systems II	W	6 credits	4G	M. Morari
Abstract	Introduction to basic and advanced concepts of modern feedback control.
Objective	Introduction to basic and advanced concepts of modern feedback control.
Content	This course is designed as a direct continuation of the course "Regelsysteme" (Feedback Control). The primary goal is to further familiarize students with various dynamic phenomena and their implications for the analysis and design of feedback controllers. Simplifying assumptions on the underlying plant that were made in the course "Regelsysteme" are relaxed, and advanced concepts and techniques that allow to treat typical industrial control problems are presented. Topics include control of systems with multiple inputs and outputs, control of uncertain systems (robustness issues), limits of achievable performance and controller implementation issues.
Lecture notes	Copy of transparencies
Literature	Skogestad, Postlethwaite: Multivariable Feedback Control - Analysis and Design. Second Edition. John Wiley, 2005.
Prerequisites / Notice	Prerequisites: Control Systems or equivalent

Page 1 of 6 All