Suchergebnis: Katalogdaten im Herbstsemester 2020

Elektrotechnik und Informationstechnologie Master Information
Master-Studium (Studienreglement 2018)
Communication
The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Communication", see https://www.ee.ethz.ch/studies/main-master/areas-of-specialisation.html.

The individual study plan is subject to the tutor's approval.
Vertiefungsfächer
These specialisation courses are particularly recommended for the area of "Communication", but you are free to choose courses from any other field in agreement with your tutor.

A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme.
NummerTitelTypECTSUmfangDozierende
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
227-0103-00LRegelsysteme Information W6 KP2V + 2UF. Dörfler
KurzbeschreibungStudy of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.
LernzielStudy of concepts and methods for the mathematical description and analysis of dynamical systems. The concept of feedback. Design of control systems for single input - single output and multivariable systems.
InhaltProcess automation, concept of control. Modelling of dynamical systems - examples, state space description, linearisation, analytical/numerical solution. Laplace transform, system response for first and second order systems - effect of additional poles and zeros. Closed-loop control - idea of feedback. PID control, Ziegler - Nichols tuning. Stability, Routh-Hurwitz criterion, root locus, frequency response, Bode diagram, Bode gain/phase relationship, controller design via "loop shaping", Nyquist criterion. Feedforward compensation, cascade control. Multivariable systems (transfer matrix, state space representation), multi-loop control, problem of coupling, Relative Gain Array, decoupling, sensitivity to model uncertainty. State space representation (modal description, controllability, control canonical form, observer canonical form), state feedback, pole placement - choice of poles. Observer, observability, duality, separation principle. LQ Regulator, optimal state estimation.
LiteraturK. J. Aström & R. Murray. Feedback Systems: An Introduction for Scientists and Engineers. Princeton University Press, 2010.
R. C. Dorf and R. H. Bishop. Modern Control Systems. Prentice Hall, New Jersey, 2007.
G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Addison-Wesley, 2010.
J. Lunze. Regelungstechnik 1. Springer, Berlin, 2014.
J. Lunze. Regelungstechnik 2. Springer, Berlin, 2014.
Voraussetzungen / BesonderesPrerequisites: Signal and Systems Theory II.

MATLAB is used for system analysis and simulation.
227-0112-00LHigh-Speed Signal Propagation Information
Findet dieses Semester nicht statt.
W6 KP2V + 2UC. Bolognesi
KurzbeschreibungVerständnis der Hochgeschwindigkeits-Signalausbreitung in Mikrowellenkabel, integr. Mikrowellenschaltungen und Leiterplatten.
Da Sytemtaktfrequenzen stets in höhere GHz Bereiche vordringen, ist es notwendig die Hochgeschwindigkeits-Signalausbreitung zu verstehen, um Signalintegrität zu gewährleisten.

Der Kurs richtet sich an Interessierte an analogen/digitalen Hochgeschwindigkeitssystemen.
LernzielVerständnis der Hochgeschwindigkeits-Signalausbreitung in Verbindungsleitern, Mikrowellenkabel und integrierten Übertragungsleitungen wie zum Beispiel in integrierten Mikrowellenschaltungen und/oder Leiterplatten.

Da Systemtaktfrequenzen kontinuierlich in höhere GHz Bereiche vordringen, entwickelt sich das dringende Bedürfnis die Hochgeschwindigkeits-Signalausbreitung zu verstehen um nach wie vor eine hohe Signalintegrität zu gewährleisten, insbesondere angesichts Phänomenen wie der Intersymbol-Interferenz (ISI) und des Übersprechens.

Konzepte wie Streuparameter (oder S-Parameter) übernehmen eine Schlüsselrolle in der Charakterisierung von Netzwerken über grosse Bandbreiten. Bei hohen Frequenzen werden alle Strukturen effektiv zu "Übertragungsleitungen".

Ohne besondere Vorsicht ist es sehr wahrscheinlich, dass eine schlecht entworfene Übertragungsleitung zum Versagen des gesamten entworfenen Systems führt.

Filter werden ebenfalls behandelt, da sich herausstellt, dass einige der Probleme von verlustbehafteten Übertragungskanälen (Leitungen, Kabel, etc.) durch adäquates filtern korrigiert werden können. Ein Prozess der "Entzerrung" genannt wird.
InhaltLeitungsgleichungen der TEM-Leitung (Telegraphengleichungen). Beschreibung elektrischer Grössen auf der TEM Leitung; Reflexion im Zeit- und Frequenzbereich, Smith-Diagramm. Verhalten schwach bedämpfter Leitungen. Einfluss des Skineffekts auf Dämpfung und Impulsverzerrung. Leitungsersatzschaltungen. Gruppenlaufzeit und Dispersion. Eigenschaften gekoppelter Leitungen. Streuparameter. Butterworth-, Tschebyscheff- und Besselfilter: Einführung zum Filterentwurf mit Filterprototypen (Tiefpass, Hochpass, Bandpass, Bandsperre). Einfache aktive Filter.
SkriptSkript: Leitungen und Filter (In deutscher Sprache).
Voraussetzungen / BesonderesDie Uebungen werden auf Englisch gehalten.
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 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 SystemVerilog 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.
- FPGA design flows.
- Dedicated and general purpose architectures compared.
- How to obtain an architecture for a given processing algorithm.
- Meeting throughput, area, and power goals by way of architectural transformations.
- Hardware Description Languages (HDL) and the underlying concepts.
- SystemVerilog
- Register Transfer Level (RTL) synthesis and its limitations.
- Building blocks of digital VLSI circuits.
- Functional verification techniques and their limitations.
- Modular and largely reusable testbenches.
- 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 FPGAs with SystemVerilog. They write testbenches for simulation purposes and synthesize gate-level netlists for 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/
227-0166-00LAnalog Integrated Circuits Information W6 KP2V + 2UT. Jang
KurzbeschreibungThis course provides a foundation in analog integrated circuit design based on bipolar and CMOS technologies.
LernzielIntegrated circuits are responsible for much of the progress in electronics in the last 50 years, particularly the revolutions in the Information and Communications Technologies we witnessed in recent years. Analog integrated circuits play a crucial part in the highly integrated systems that power the popular electronic devices we use daily. Understanding their design is beneficial to both future designers and users of such systems.
The basic elements, design issues and techniques for analog integrated circuits will be taught in this course.
InhaltReview of bipolar and MOS devices and their small-signal equivalent circuit models; Building blocks in analog circuits such as current sources, active load, current mirrors, 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; data converters; frequency synthesizers; switched capacitors.
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 measurements.
SkriptHandouts of presented slides. No script but an accompanying textbook is recommended.
LiteraturBehzad Razavi, Design of Analog CMOS Integrated Circuits (Irwin Electronics & Computer Engineering) 1st or 2nd edition, McGraw-Hill Education
227-0301-00LOptical Communication FundamentalsW6 KP2V + 1U + 1PJ. Leuthold
KurzbeschreibungThe path of an analog signal in the transmitter to the digital world in a communication link and back to the analog world at the receiver is discussed. The lecture covers the fundamentals of all important optical and optoelectronic components in a fiber communication system. This includes the transmitter, the fiber channel and the receiver with the electronic digital signal processing elements.
LernzielAn in-depth understanding on how information is transmitted from source to destination. Also the mathematical framework to describe the important elements will be passed on. Students attending the lecture will further get engaged in critical discussion on societal, economical and environmental aspects related to the on-going exponential growth in the field of communications.
Inhalt* Chapter 1: Introduction: Analog/Digital conversion, The communication channel, Shannon channel capacity, Capacity requirements.

* Chapter 2: The Transmitter: Components of a transmitter, Lasers, The spectrum of a signal, Optical modulators, Modulation formats.

* Chapter 3: The Optical Fiber Channel: Geometrical optics, The wave equations in a fiber, Fiber modes, Fiber propagation, Fiber losses, Nonlinear effects in a fiber.

* Chapter 4: The Receiver: Photodiodes, Receiver noise, Detector schemes (direct detection, coherent detection), Bit-error ratios and error estimations.

* Chapter 5: Digital Signal Processing Techniques: Digital signal processing in a coherent receiver, Error detection teqchniques, Error correction coding.

* Chapter 6: Pulse Shaping and Multiplexing Techniques: WDM/FDM, TDM, OFDM, Nyquist Multiplexing, OCDMA.

* Chapter 7: Optical Amplifiers : Semiconductor Optical Amplifiers, Erbium Doped Fiber Amplifiers, Raman Amplifiers.
SkriptLecture notes are handed out.
LiteraturGovind P. Agrawal; "Fiber-Optic Communication Systems"; Wiley, 2010
Voraussetzungen / BesonderesFundamentals of Electromagnetic Fields & Bachelor Lectures on Physics.
227-0377-10LPhysics of Failure and Reliability of Electronic Devices and SystemsW3 KP2VI. Shorubalko, M. Held
KurzbeschreibungUnderstanding the physics of failures and failure mechanisms enables reliability analysis and serves as a practical guide for electronic devices design, integration, systems development and manufacturing. The field gains additional importance in the context of managing safety, sustainability and environmental impact for continuously increasing complexity and scaling-down trends in electronics.
LernzielProvide an understanding of the physics of failure and reliability. Introduce the degradation and failure mechanisms, basics of failure analysis, methods and tools of reliability testing.
InhaltSummary of reliability and failure analysis terminology; physics of failure: materials properties, physical processes and failure mechanisms; failure analysis; basics and properties of instruments; quality assurance of technical systems (introduction); introduction to stochastic processes; reliability analysis; component selection and qualification; maintainability analysis (introduction); design rules for reliability, maintainability, reliability tests (introduction).
SkriptComprehensive copy of transparencies
LiteraturReliability Engineering: Theory and Practice, 8th Edition, Springer 2017, DOI 10.1007/978-3-662-54209-5
Reliability Engineering: Theory and Practice, 8th Edition (2017), DOI 10.1007/978-3-662-54209-5
227-0423-00LNeural Network Theory Information W4 KP2V + 1UH. Bölcskei
KurzbeschreibungThe class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal approximation theorems, basics of approximation theory, fundamental limits of deep neural network learning, geometry of decision surfaces, capacity of separating surfaces, dimension measures relevant for generalization, VC dimension of neural networks.
LernzielAfter attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the mathematical foundations of (deep) neural networks.
Inhalt1. Universal approximation with single- and multi-layer networks

2. Introduction to approximation theory: Fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, non-linear approximation theory

3. Fundamental limits of deep neural network learning

4. Geometry of decision surfaces

5. Separating capacity of nonlinear decision surfaces

6. Dimension measures: Pseudo-dimension, fat-shattering dimension, Vapnik-Chervonenkis (VC) dimension

7. Dimensions of neural networks

8. Generalization error in neural network learning
SkriptDetailed lecture notes will be provided.
Voraussetzungen / BesonderesThis course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular.
227-0447-00LImage Analysis and Computer Vision Information W6 KP3V + 1UL. Van Gool, E. Konukoglu, F. Yu
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.
227-0468-00LAnalog Signal Processing and Filtering Information
Suitable for Master Students as well as Doctoral Students.
W6 KP2V + 2UH. Schmid
KurzbeschreibungThis lecture provides a wide overview over analog filters (continuous-time and discrete-time), signal-processing systems, and sigma-delta conversion, and gives examples with sensor interfaces and class-D audio drivers. All systems and circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers.
LernzielThis lecture provides a wide overview over analog filters (continuous-time and discrete-time), signal-processing systems, and sigma-delta conversion, and gives examples with sensor interfaces and class-D audio drivers. All systems and circuits are treated using a signal-flow view. The lecture is suitable for both analog and digital designers. The way the exam is done allows for the different interests of the two groups.

The learning goal is that the students can apply signal-flow graphs and can understand the signal flow in such circuits and systems (including non-ideal effects) well enough to gain an understanding of further circuits and systems by themselves.
InhaltAt the beginning, signal-flow graphs in general and driving-point signal-flow graphs in particular are introduced. We will use them during the whole term to analyze circuits on a system level (analog continuous-time, analog discrete-time, mixed-signal and digital) and understand how signals propagate through them. The theory and CMOS implementation of active Filters is then discussed in detail using the example of Gm-C filters and active-RC filters. The ideal and nonideal behaviour of opamps, current conveyors, and inductor simulators follows. The link to the practical design of circuits and systems is done with an overview over different quality measures and figures of merit used in scientific literature and datasheets. Finally, an introduction to discrete-time and mixed-domain filters and circuits is given, including sensor read-out amplifiers, correlated double sampling, and chopping, and an introduction to sigma-delta A/D and D/A conversion on a system level.

This lecture does not go down to the details of transistor implementations. The lecture "227-0166-00L Analog Integrated Circuits" complements This lecture very well in that respect.
SkriptThe base for these lectures are lecture notes and two or three published scientific papers. From these papers we will together develop the technical content.

Details: https://people.ee.ethz.ch/~haschmid/asfwiki/

The graph methods are also supported with teaching videos: https://tube.switch.ch/channels/d206c96c?order=episodes

Some material is protected by password; students from ETHZ who are interested can write to haschmid@ethz.ch to ask for the password even if they do not attend the lecture.
Voraussetzungen / BesonderesLive stream: due to Covids rules, the lecture will be streamed live. Join here: https://www.twitch.tv/hanspi42/

Prerequisites: Recommended (but not required): Stochastic models and signal processing, Communication Electronics, Analog Integrated Circuits, Transmission Lines and Filters.

Knowledge of the Laplace transform and z transform and their interpretation (transfer functions, poles and zeros, bode diagrams, stability criteria ...) and of the main properties of linear systems is necessary.
227-0477-00LAcoustics IW6 KP4GK. Heutschi
KurzbeschreibungIntroduction to the fundamentals of acoustics in the area of sound field calculations, measurement of acoustical events, outdoor sound propagation and room acoustics of large and small enclosures.
LernzielIntroduction to acoustics. Understanding of basic acoustical mechanisms. Survey of the technical literature. Illustration of measurement techniques in the laboratory.
InhaltFundamentals of acoustics, measuring and analyzing of acoustical events, anatomy and properties of the ear. Outdoor sound propagation, absorption and transmission of sound, room acoustics of large and small enclosures, architectural acoustics, noise and noise control, calculation of sound fields.
Skriptyes
252-0535-00LAdvanced Machine Learning Information W10 KP3V + 2U + 4AJ. M. Buhmann, C. Cotrini Jimenez
KurzbeschreibungMachine learning algorithms provide analytical methods to search data sets for characteristic patterns. Typical tasks include the classification of data, function fitting and clustering, with applications in image and speech analysis, bioinformatics and exploratory data analysis. This course is accompanied by practical machine learning projects.
LernzielStudents will be familiarized with advanced concepts and algorithms for supervised and unsupervised learning; reinforce the statistics knowledge which is indispensible to solve modeling problems under uncertainty. Key concepts are the generalization ability of algorithms and systematic approaches to modeling and regularization. Machine learning projects will provide an opportunity to test the machine learning algorithms on real world data.
InhaltThe theory of fundamental machine learning concepts is presented in the lecture, and illustrated with relevant applications. Students can deepen their understanding by solving both pen-and-paper and programming exercises, where they implement and apply famous algorithms to real-world data.

Topics covered in the lecture include:

Fundamentals:
What is data?
Bayesian Learning
Computational learning theory

Supervised learning:
Ensembles: Bagging and Boosting
Max Margin methods
Neural networks

Unsupservised learning:
Dimensionality reduction techniques
Clustering
Mixture Models
Non-parametric density estimation
Learning Dynamical Systems
SkriptNo lecture notes, but slides will be made available on the course webpage.
LiteraturC. Bishop. Pattern Recognition and Machine Learning. Springer 2007.

R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley &
Sons, second edition, 2001.

T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical
Learning: Data Mining, Inference and Prediction. Springer, 2001.

L. Wasserman. All of Statistics: A Concise Course in Statistical
Inference. Springer, 2004.
Voraussetzungen / BesonderesThe course requires solid basic knowledge in analysis, statistics and numerical methods for CSE as well as practical programming experience for solving assignments.
Students should have followed at least "Introduction to Machine Learning" or an equivalent course offered by another institution.

PhD students are required to obtain a passing grade in the course (4.0 or higher based on project and exam) to gain credit points.
263-4640-00LNetwork Security Information W8 KP2V + 2U + 3AA. Perrig, S. Frei, M. Legner
KurzbeschreibungSome of today's most damaging attacks on computer systems involve
exploitation of network infrastructure, either as the target of attack
or as a vehicle to attack end systems. This course provides an
in-depth study of network attack techniques and methods to defend
against them.
Lernziel- Students are familiar with fundamental network security concepts.
- Students can assess current threats that Internet services and networked devices face, and can evaluate appropriate countermeasures.
- Students can identify and assess known vulnerabilities in a software system that is connected to the Internet (through analysis and penetration testing tools).
- Students have an in-depth understanding of a range of important security technologies.
- Students learn how formal analysis techniques can help in the design of secure networked systems.
InhaltThe course will cover topics spanning five broad themes: (1) network
defense mechanisms such as secure routing protocols, TLS, anonymous
communication systems, network intrusion detection systems, and
public-key infrastructures; (2) network attacks such as denial of
service (DoS) and distributed denial-of-service (DDoS) attacks; (3)
analysis and inference topics such as network forensics and attack
economics; (4) formal analysis techniques for verifying the security
properties of network architectures; and (5) new technologies related
to next-generation networks.
Voraussetzungen / BesonderesThis lecture is intended for students with an interest in securing
Internet communication services and network devices. Students are
assumed to have knowledge in networking as taught in a Communication
Networks lecture. The course will involve a course project and some
smaller programming projects as part of the homework. Students are
expected to have basic knowledge in network programming in a
programming language such as C/C++, Go, or Python.
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