Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Doktorat Departement Informationstechnologie und Elektrotechnik Information
Mehr Informationen unter: https://www.ethz.ch/de/doktorat.html
Lehrangebot Doktorat und Postdoktorat
A minimum of 12 ECTS credit points must be obtained during doctoral studies.

The courses on offer below are but a small selection out of a much larger available number of courses. Please discuss your course selection with your PhD supervisor.
NummerTitelTypECTSUmfangDozierende
151-0906-00LFrontiers in Energy Research Information
This course is only for doctoral students.
W2 KP2SC. Schaffner
KurzbeschreibungDoctoral students at ETH Zurich working in the broad area of energy present their research to their colleagues, their advisors and the scientific community. Each week a different student gives a 50-60 min presentation of their research (a full introduction, background & findings) followed by discussion with the audience.
LernzielThe key objectives of the course are:
(1) participants will gain knowledge of advanced research in the area of energy;
(2) participants will actively participate in discussion after each presentation;
(3) participants gain experience of different presentation styles;
(4) to create a network amongst the energy research doctoral student community.
InhaltDoctoral students at ETH Zurich working in the broad area of energy present their research to their colleagues, to their advisors and to the scientific community. There will be one presentation a week during the semester, each structured as follows: 20 min introduction to the research topic, 30 min presentation of the results, 30 min discussion with the audience.
SkriptSlides will be available on the Energy Science Center pages(www.esc.ethz.ch/events/frontiers-in-energy-research.html).
» Auswahl aus sämtlichen Lehrveranstaltungen der ETH Zürich
227-0126-00LAdvanced Topics in Networked Embedded SystemsW2 KP1SL. Thiele, J. Beutel
KurzbeschreibungThe seminar will cover advanced topics in networked embedded systems. A particular focus are cyber-physical systems, internet of things, and sensor networks in various application domains.
LernzielThe goal is to get a deeper understanding on leading edge technologies in the discipline, on classes of applications, and on current as well as future research directions. In addition, participants will improve their presentation, reading and reviewing skills.
InhaltThe seminar enables Master students, PhDs and Postdocs to learn about latest breakthroughs in wireless sensor networks, networked embedded systems and devices, and energy-harvesting in several application domains, including environmental monitoring, tracking, smart buildings and control. Participants are requested to actively participate in the organization and preparation of the seminar. In particular, they review all presented papers using a standard scientific reviewing system, they present one of the papers orally and they lead the corresponding discussion session.
227-0146-00LAnalog-to-Digital Converters Information
Findet dieses Semester nicht statt.
Course will be moved to the fall semester 2021.
W6 KP2V + 2U
KurzbeschreibungThis course provides a thorough treatment of integrated data conversion systems from system level specifications and trade-offs, over architecture choice down to circuit implementation.
LernzielData conversion systems are substantial sub-parts of many electronic systems, e.g. the audio conversion system of a home-cinema systems or the base-band front-end of a wireless modem. Data conversion systems usually determine the performance of the overall system in terms of dynamic range and linearity. The student will learn to understand the basic principles behind data conversion and be introduced to the different methods and circuit architectures to implement such a conversion. The conversion methods such as successive approximation or algorithmic conversion are explained with their principle of operation accompanied with the appropriate mathematical calculations, including the effects of non-idealties in some cases. After successful completion of the course the student should understand the concept of an ideal ADC, know all major converter architectures, their principle of operation and what governs their performance.
Inhalt- Introduction: information representation and communication; abstraction, categorization and symbolic representation; basic conversion algorithms; data converter application; tradeoffs among key parameters; ADC taxonomy.
- Dual-slope & successive approximation register (SAR) converters: dual slope principle & converter; SAR ADC operating principle; SAR implementation with a capacitive array; range extension with segmented array.
- Algorithmic & pipelined A/D converters: algorithmic conversion principle; sample & hold stage; pipe-lined converter; multiplying DAC; flash sub-ADC and n-bit MDAC; redundancy for correction of non-idealties, error correction.
- Performance metrics and non-linearity: ideal ADC; offset, gain error, differential and integral non-linearities; capacitor mismatch; impact of capacitor mismatch on SAR ADC's performance.
- Flash, folding an interpolating analog-to-digital converters: flash ADC principle, thermometer to binary coding, sparkle correction; limitations of flash converters; the folding principle, residue extraction; folding amplifiers; cascaded folding; interpolation for folding converters; cascaded folding and interpolation.
- Noise in analog-to-digital converters: types of noise; noise calculation in electronic circuit, kT/C-noise, sampled noise; noise analysis in switched-capacitor circuits; aperture time uncertainty and sampling jitter.
- Delta-sigma A/D-converters: linearity and resolution; from delta-modulation to delta-sigma modulation; first-oder delta-sigma modulation, circuit level implementation; clock-jitter & SNR in delta-sigma modulators; second-order delta-sigma modulation, higher-order modulation, design procedure for a single-loop modulator.
- Digital-to-analog converters: introduction; current scaling D/A converter, current steering DAC, calibration for improved performance.
SkriptSlides are available online under https://iis-students.ee.ethz.ch/lectures/analog-to-digital-converters/
Literatur- B. Razavi, Principles of Data Conversion System Design, IEEE Press, 1994
- M. Gustavsson et. al., CMOS Data Converters for Communications, Springer, 2010
- R.J. van de Plassche, CMOS Integrated Analog-to-Digital and Digital-to-Analog Converters, Springer, 2010
Voraussetzungen / BesonderesIt is highly recommended to attend the course "Analog Integrated Circuits" of Prof. Huang as a preparation for this course.
227-0159-00LSemiconductor Devices: Quantum Transport at the Nanoscale Information W6 KP2V + 2UM. Luisier, A. Emboras
KurzbeschreibungThis class offers an introduction into quantum transport theory, a rigorous approach to electron transport at the nanoscale. It covers different topics such as bandstructure, Wave Function and Non-equilibrium Green's Function formalisms, and electron interactions with their environment. Matlab exercises accompany the lectures where students learn how to develop their own transport simulator.
LernzielThe continuous scaling of electronic devices has given rise to structures whose dimensions do not exceed a few atomic layers. At this size, electrons do not behave as particle any more, but as propagating waves and the classical representation of electron transport as the sum of drift-diffusion processes fails. The purpose of this class is to explore and understand the displacement of electrons through nanoscale device structures based on state-of-the-art quantum transport methods and to get familiar with the underlying equations by developing his own nanoelectronic device simulator.
InhaltThe following topics will be addressed:
- Introduction to quantum transport modeling
- Bandstructure representation and effective mass approximation
- Open vs closed boundary conditions to the Schrödinger equation
- Comparison of the Wave Function and Non-equilibrium Green's Function formalisms as solution to the Schrödinger equation
- Self-consistent Schödinger-Poisson simulations
- Quantum transport simulations of resonant tunneling diodes and quantum well nano-transistors
- Top-of-the-barrier simulation approach to nano-transistor
- Electron interactions with their environment (phonon, roughness, impurity,...)
- Multi-band transport models
SkriptLecture slides are distributed every week and can be found at
https://iis-students.ee.ethz.ch/lectures/quantum-transport-in-nanoscale-devices/
LiteraturRecommended textbook: "Electronic Transport in Mesoscopic Systems", Supriyo Datta, Cambridge Studies in Semiconductor Physics and Microelectronic Engineering, 1997
Voraussetzungen / BesonderesBasic knowledge of semiconductor device physics and quantum mechanics
227-0207-00LNonlinear Systems and Control Information
Voraussetzung: Control Systems (227-0103-00L)
W6 KP4GE. Gallestey Alvarez, P. F. Al Hokayem
KurzbeschreibungIntroduction to the area of nonlinear systems and their control. Familiarization with tools for analysis of nonlinear systems. Discussion of the various nonlinear controller design methods and their applicability to real life problems.
LernzielOn completion of the course, students understand the difference between linear and nonlinear systems, know the mathematical techniques for analysing these systems, and have learnt various methods for designing controllers accounting for their characteristics.

Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of the virtues and drawbacks present in the different methods.
InhaltVirtually all practical control problems are of nonlinear nature. In some cases application of linear control methods leads to satisfactory controller performance. In many other cases however, only application of nonlinear analysis and control synthesis methods will guarantee achievement of the desired objectives.

During the past decades mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons. Along the course the students will be familiarized with the basic concepts of nonlinear control theory.

This course is designed as an introduction to the nonlinear control field and thus no prior knowledge of this area is required. The course builds, however, on a good knowledge of the basic concepts of linear control and mathematical analysis.
SkriptAn english manuscript will be made available on the course homepage during the course.
LiteraturH.K. Khalil: Nonlinear Systems, Prentice Hall, 2001.
Voraussetzungen / BesonderesPrerequisites: Linear Control Systems, or equivalent.
151-0660-00LModel Predictive Control Information W4 KP2V + 1UM. Zeilinger
KurzbeschreibungModel predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.
LernzielDesign and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.
Inhalt- Review of required optimal control theory
- Basics on optimization
- Receding-horizon control (MPC) for constrained linear systems
- Theoretical properties of MPC: Constraint satisfaction and stability
- Computation: Explicit and online MPC
- Practical issues: Tracking and offset-free control of constrained systems, soft constraints
- Robust MPC: Robust constraint satisfaction
- Nonlinear MPC: Theory and computation
- Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization
- Simulation-based project providing practical experience with MPC
SkriptScript / lecture notes will be provided.
Voraussetzungen / BesonderesOne semester course on automatic control, Matlab, linear algebra.
Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control.

Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).
227-0418-00LAlgebra and Error Correcting Codes Information W6 KP4GH.‑A. Loeliger
KurzbeschreibungThe course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.
LernzielThe course is an introduction to error correcting codes covering both classical algebraic codes and modern iterative decoding. The course includes a self-contained introduction of the pertinent basics of "abstract" algebra.
InhaltError correcting codes: 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, polar codes, Reed-Solomon codes.

Algebra: groups, rings, homomorphisms, quotient groups, ideals, finite fields, vector spaces, polynomials.
SkriptLecture Notes (english)
227-0420-00LInformation Theory II Information
Findet dieses Semester nicht statt.
W6 KP2V + 2UA. Lapidoth
KurzbeschreibungThis course builds on Information Theory I. It introduces additional topics in single-user communication, connections between Information Theory and Statistics, and Network Information Theory.
LernzielThe course has two objectives: to introduce the students to the key information theoretic results that underlay the design of communication systems and to equip the students with the tools that are needed to conduct research in Information Theory.
InhaltDifferential entropy, maximum entropy, the Gaussian channel and water filling, the entropy-power inequality, Sanov's Theorem, Fisher information, the broadcast channel, the multiple-access channel, Slepian-Wolf coding, and the Gelfand-Pinsker problem.
Skriptn/a
LiteraturT.M. Cover and J.A. Thomas, Elements of Information Theory, second edition, Wiley 2006
227-0455-00LTerahertz: Technology and ApplicationsW5 KP3G + 3AK. Sankaran
KurzbeschreibungThis block course will provide a solid foundation for understanding physical principles of THz applications. We will discuss various building blocks of THz technology - components dealing with generation, manipulation, and detection of THz electromagnetic radiation. We will introduce THz applications in the domain of imaging, sensing, communications, non-destructive testing and evaluations.
LernzielThis is an introductory course on Terahertz (THz) technology and applications. Devices operating in THz frequency range (0.1 to 10 THz) have been increasingly studied in the recent years. Progress in nonlinear optical materials, ultrafast optical and electronic techniques has strengthened research in THz application developments. Due to unique interaction of THz waves with materials, applications with new capabilities can be developed. In theory, they can penetrate somewhat like X-rays, but are not considered harmful radiation, because THz energy level is low. They should be able to provide resolution as good as or better than magnetic resonance imaging (MRI), possibly with simpler equipment. Imaging, very-high bandwidth communication, and energy harvesting are the most widely explored THz application areas. We will study the basics of THz generation, manipulation, and detection. Our emphasis will be on the physical principles and applications of THz in the domain of imaging, sensing, communications, non-destructive testing and evaluations.

The second part of the block course will be a short project work related to the topics covered in the lecture. The learnings from the project work should be presented in the end.
InhaltPART I:

- INTRODUCTION -
Chapter 1: Introduction to THz Physics
Chapter 2: Components of THz Technology

- THz TECHNOLOGY MODULES -
Chapter 3: THz Generation
Chapter 4: THz Detection
Chapter 5: THz Manipulation

- APPLICATIONS -
Chapter 6: THz Imaging / Sensing / Communication
Chapter 7: THz Non-destructive Testing
Chapter 8: THz Applications in Plastic & Recycling Industries

PART 2:

- PROJECT WORK -
Short project work related to the topics covered in the lecture.
Short presentation of the learnings from the project work.
Full guidance and supervision will be given for successful completion of the short project work.
SkriptSoft-copy of lectures notes will be provided.
Literatur- Yun-Shik Lee, Principles of Terahertz Science and Technology, Springer 2009
- Ali Rostami, Hassan Rasooli, and Hamed Baghban, Terahertz Technology: Fundamentals and Applications, Springer 2010
Voraussetzungen / BesonderesBasic foundation in physics, particularly, electromagnetics is required.
Students who want to refresh their electromagnetics fundamentals can get additional material required for the course.
402-0448-01LQuantum Information Processing I: Concepts
Dieser theoretisch ausgerichtete Teil QIP I bildet zusammen mit dem experimentell ausgerichteten Teil 402-0448-02L QIP II, die beide im Frühjahrssemester angeboten werden, im Master-Studiengang Physik das experimentelle Kernfach "Quantum Information Processing" mit total 10 ECTS-Kreditpunkten.
W5 KP2V + 1UP. Kammerlander
KurzbeschreibungThe course will cover the key concepts and ideas of quantum information processing, including descriptions of quantum algorithms which give the quantum computer the power to compute problems outside the reach of any classical supercomputer.
Key concepts such as quantum error correction will be described. These ideas provide fundamental insights into the nature of quantum states and measurement.
LernzielWe aim to provide an overview of the central concepts in Quantum Information Processing, including insights into the advantages to be gained from using quantum mechanics and the range of techniques based on quantum error correction which enable the elimination of noise.
InhaltThe topics covered in the course will include quantum circuits, gate decomposition and universal sets of gates, efficiency of quantum circuits, quantum algorithms (Shor, Grover, Deutsch-Josza,..), error correction, fault-tolerant design, entanglement, teleportation and dense conding, teleportation of gates, and cryptography.
SkriptMore details to follow.
LiteraturQuantum Computation and Quantum Information
Michael Nielsen and Isaac Chuang
Cambridge University Press
Voraussetzungen / BesonderesBasic knowledge in the formalism of quantum states, unitary evolution and quantum measurement is recommended.
402-0448-02LQuantum Information Processing II: Implementations
Dieser experimentell ausgerichtete Teil QIP II bildet zusammen mit dem theoretisch ausgerichteten Teil 402-0448-01L QIP I, die beide im Frühjahrssemester angeboten werden, im Master-Studiengang Physik das experimentelle Kernfach "Quantum Information Processing" mit total 10 ECTS-Kreditpunkten.
W5 KP2V + 1UJ. Home
KurzbeschreibungIntroduction to experimental systems for quantum information processing (QIP). Quantum bits. Coherent Control. Measurement. Decoherence. Microscopic and macroscopic quantum systems. Nuclear magnetic resonance (NMR). Photons. Ions and neutral atoms in electromagnetic traps. Charges and spins in quantum dots and NV centers. Charges and flux quanta in superconducting circuits. Novel hybrid systems.
LernzielThroughout the past 20 years the realm of quantum physics has entered the domain of information technology in more and more prominent ways. Enormous progress in the physical sciences and in engineering and technology has allowed us to build novel types of information processors based on the concepts of quantum physics. In these processors information is stored in the quantum state of physical systems forming quantum bits (qubits). The interaction between qubits is controlled and the resulting states are read out on the level of single quanta in order to process information. Realizing such challenging tasks is believed to allow constructing an information processor much more powerful than a classical computer. This task is taken on by academic labs, startups and major industry. The aim of this class is to give a thorough introduction to physical implementations pursued in current research for realizing quantum information processors. The field of quantum information science is one of the fastest growing and most active domains of research in modern physics.
InhaltIntroduction to experimental systems for quantum information processing (QIP).
- Quantum bits
- Coherent Control
- Measurement
- Decoherence
QIP with
- Ions
- Superconducting Circuits
- Photons
- NMR
- Rydberg atoms
- NV-centers
- Quantum dots
SkriptCourse material be made available at www.qudev.ethz.ch and on the Moodle platform for the course. More details to follow.
LiteraturQuantum Computation and Quantum Information
Michael Nielsen and Isaac Chuang
Cambridge University Press
Voraussetzungen / BesonderesThe class will be taught in English language.

Basic knowledge of concepts of quantum physics and quantum systems, e.g from courses such as Phyiscs III, Quantum Mechanics I and II or courses on topics such as atomic physics, solid state physics, quantum electronics are considered helpful.

More information on this class can be found on the web site www.qudev.ethz.ch
227-0558-00LPrinciples of Distributed Computing Information W7 KP2V + 2U + 2AR. Wattenhofer, M. Ghaffari
KurzbeschreibungWe study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.
LernzielDistributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, 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.
InhaltDistributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems.

Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds
SkriptAvailable. Our course script is used at dozens of other universities around the world.
LiteraturLecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world.

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
Voraussetzungen / BesonderesCourse pre-requisites: Interest in algorithmic problems. (No particular course needed.)
227-0662-00LOrganic and Nanostructured Optics and Electronics (Course)
Findet dieses Semester nicht statt.
W3 KP2GV. Wood
KurzbeschreibungThis course examines the optical and electronic properties of excitonic materials that can be leveraged to create thin-film light emitting devices and solar cells. Laboratory sessions provide students with experience in synthesis and optical characterization of nanomaterials as well as fabrication and characterization of thin film devices.
LernzielGain the knowledge and practical experience to begin research with organic or nanostructured materials and understand the key challenges in this rapidly emerging field.
Inhalt0-Dimensional Excitonic Materials (organic molecules and colloidal quantum dots)

Energy Levels and Excited States (singlet and triplet states, optical absorption and luminescence).

Excitonic and Polaronic Processes (charge transport, Dexter and Förster energy transfer, and exciton diffusion).

Devices (photodetectors, solar cells, and light emitting devices).
LiteraturLecture notes and reading assignments from current literature to be posted on website.
227-0662-10LOrganic and Nanostructured Optics and Electronics (Project) Information Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
W3 KP2AV. Wood
KurzbeschreibungThis course examines the optical and electronic properties of excitonic materials that can be leveraged to create thin-film light emitting devices and solar cells. Laboratory sessions provide students with experience in synthesis and optical characterization of nanomaterials as well as fabrication and characterization of thin film devices.
LernzielGain the knowledge and practical experience to begin research with organic or nanostructured materials and understand the key challenges in this rapidly emerging field.
Inhalt0-Dimensional Excitonic Materials (organic molecules and colloidal quantum dots)

Energy Levels and Excited States (singlet and triplet states, optical absorption and luminescence).

Excitonic and Polaronic Processes (charge transport, Dexter and Förster energy transfer, and exciton diffusion).

Devices (photodetectors, solar cells, and light emitting devices).
LiteraturLecture notes and reading assignments from current literature to be posted on website.
Voraussetzungen / BesonderesAdmission is conditional to passing 227-0662-00L Organic and Nanostructured Optics and Electronics (Course)
227-0946-00LMolecular Imaging - Basic Principles and Biomedical ApplicationsW2 KP2VM. Rudin
KurzbeschreibungConcept: What is molecular imaging.
Discussion/comparison of the various imaging modalities used in molecular imaging.
Design of target specific probes: specificity, delivery, amplification strategies.
Biomedical Applications.
LernzielMolecular Imaging is a rapidly emerging discipline that translates concepts developed in molecular biology and cellular imaging to in vivo imaging in animals and ultimatly in humans. Molecular imaging techniques allow the study of molecular events in the full biological context of an intact organism and will therefore become an indispensable tool for biomedical research.
InhaltConcept: What is molecular imaging.
Discussion/comparison of the various imaging modalities used in molecular imaging.
Design of target specific probes: specificity, delivery, amplification strategies.
Biomedical Applications.
227-0974-00LTNU Colloquium Belegung eingeschränkt - Details anzeigen W0 KP2KK. Stephan
KurzbeschreibungThis colloquium discusses research topics in Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and Computational Psychiatry/Psychosomatics (the application of these models to concrete clinical questions). The range of topics is broad, incl. computational techniques, experimental paradigms (fMRI, EEG, behaviour), and clinical questions.
Lernzielsee above
227-0434-10LMathematics of Information Information W8 KP3V + 2U + 2AH. Bölcskei
KurzbeschreibungThe class focuses on mathematical aspects of

1. Information science: Sampling theorems, frame theory, compressed sensing, sparsity, super-resolution, spectrum-blind sampling, subspace algorithms, dimensionality reduction

2. Learning theory: Approximation theory, uniform laws of large numbers, Rademacher complexity, Vapnik-Chervonenkis dimension
LernzielThe aim of the class is to familiarize the students with the most commonly used mathematical theories in data science, high-dimensional data analysis, and learning theory. The class consists of the lecture, exercise sessions with homework problems, and of a research project, which can be carried out either individually or in groups. The research project consists of either 1. software development for the solution of a practical signal processing or machine learning problem or 2. the analysis of a research paper or 3. a theoretical research problem of suitable complexity. Students are welcome to propose their own project at the beginning of the semester. The outcomes of all projects have to be presented to the entire class at the end of the semester.
InhaltMathematics of Information

1. Signal representations: Frame theory, wavelets, Gabor expansions, sampling theorems, density theorems

2. Sparsity and compressed sensing: Sparse linear models, uncertainty relations in sparse signal recovery, matching pursuits, super-resolution, spectrum-blind sampling, subspace algorithms (MUSIC, ESPRIT, matrix pencil), estimation in the high-dimensional noisy case, Lasso

3. Dimensionality reduction: Random projections, the Johnson-Lindenstrauss Lemma

Mathematics of Learning

4. Approximation theory: Nonlinear approximation theory, fundamental limits on compressibility of signal classes, Kolmogorov-Tikhomirov epsilon-entropy of signal classes, optimal compression of signal classes, recovery from incomplete data, information-based complexity, curse of dimensionality

5. Uniform laws of large numbers: Rademacher complexity, Vapnik-Chervonenkis dimension, classes with polynomial discrimination, blessings of dimensionality
SkriptDetailed lecture notes will be provided at the beginning of the semester and as we go along.
Voraussetzungen / BesonderesThis course is aimed at students with a background in basic linear algebra, analysis, statistics, and probability.

We encourage students who are interested in mathematical data science to take both this course and "401-4944-20L Mathematics of Data Science" by Prof. A. Bandeira. The two courses are designed to be complementary.

H. Bölcskei and A. Bandeira
227-0928-00LDistinguished Lecture Series in Control Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
W1 KP1VF. Dörfler
KurzbeschreibungThis seminar introduces students to advanced scientific methods system theory, automatic control and optimization. The seminar is primarily delivered by an external distinguished speaker and its contents will be tailored towards doctoral and research-interested students. The detailed coverage varies every semester.
LernzielThe intent is to introduce students to advanced scientific methods in the areas of system theory, automatic control, and optimization. The seminar is jointly by Prof. F. Dörfler, it will be primarily delivered by an external distinguished speaker, and its contents will be tailored towards doctoral and research-interested students. The detailed coverage varies from semester to semester. During the Spring Semester 2019 the seminar will concentrate on robust and adaptive output regulation of multivariable and hybrid systems, and it will be delivered by Prof. Alberto Isidori and Prof. Lorenzo Marconi.
InhaltIn spring 2019 the seminar will concentrate on robust and adaptive output regulation of multivariable and hybrid systems.
252-0312-00LUbiquitous Computing Information W4 KP2V + 1AC. Holz, F. Mattern, S. Mayer
KurzbeschreibungUnlike desktop computing, ubiquitous computing occurs anytime and everywhere, using any device, in any location, and in any format. Computers exist in different forms, from watches and phones to refrigerators or pairs of glasses.
Main topics: Smart environments, IoT, mobiles & wearables, context & location, sensing & tracking, computer vision on embedded systems, health monitoring, fabrication.
LernzielUnlike desktop computing, ubiquitous computing occurs anytime and everywhere, using any device, in any location, and in any format. Computers exist in different forms, from watches and phones to refrigerators or pairs of glasses.
Main topics: Smart environments, IoT, mobiles & wearables, context & location, sensing & tracking, computer vision on embedded systems, health monitoring, fabrication.
SkriptCopies of slides will be made available
LiteraturWill be provided in the lecture. To put you in the mood:
Mark Weiser: The Computer for the 21st Century. Scientific American, September 1991, pp. 94-104
227-0559-00LSeminar in Deep Reinforcement Learning Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 25.
W2 KP2SR. Wattenhofer, O. Richter
KurzbeschreibungIn this seminar participating students present and discuss recent research papers in the area of deep reinforcement learning. The seminar starts with two introductory lessons introducing the basic concepts. Alongside the seminar a programming challenge is posed in which students can take part to improve their grade.
LernzielSince Google Deepmind presented the Deep Q-Network (DQN) algorithm in 2015 that could play Atari-2600 games at a superhuman level, the field of deep reinforcement learning gained a lot of traction. It sparked media attention with AlphaGo and AlphaZero and is one of the most prominent research areas. Yet many research papers in the area come from one of two sources: Google Deepmind or OpenAI. In this seminar we aim at giving the students an in depth view on the current advances in the area by discussing recent papers as well as discussing current issues and difficulties surrounding deep reinforcement learning.
InhaltTwo introductory courses introducing Q-learning and policy gradient methods. Afterwards participating students present recent papers. For details see: www.disco.ethz.ch/courses.html
SkriptSlides of presentations will be made available.
LiteraturOpenAI course (https://spinningup.openai.com/en/latest/) plus selected papers.
The paper selection can be found on www.disco.ethz.ch/courses.html.
Voraussetzungen / BesonderesIt is expected that student have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses.
227-0690-11LAdvanced Topics in Control (Spring 2020)
New topics are introduced every year.
W4 KP2V + 2UG. Banjac
KurzbeschreibungAdvanced Topics in Control (ATIC) covers advanced research topics in control theory. It is offered each Spring semester with the topic rotating from year to year. Repetition for credit is possible, with consent of the instructor.
LernzielDuring Spring 2020 the course will cover a range of topics in large-scale convex optimization. The students should be able to apply various numerical methods to solve large-scale optimization problems arising in control, machine learning, signal processing, and finance.
InhaltConvex analysis and methods for large-scale optimization. Topics will include: convex sets and functions ; duality theory ; optimality and infeasibility conditions ; structured optimization problems ; gradient-based methods ; operator splitting methods ; distributed and decentralized optimization ; applications in various research areas.
SkriptCopies of the projection slides will be made available on the course Moodle platform.
LiteraturThe course will be largely based on the Large-Scale Convex Optimization course taught at Lund University: https://archive.control.lth.se/ls-convex-2015/
Voraussetzungen / BesonderesSufficient mathematical maturity, in particular in linear algebra and analysis.
227-0559-10LSeminar in Communication Networks: Learning, Reasoning and Control Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
Number of participants limited to 24.
W2 KP2SL. Vanbever, A. Singla
KurzbeschreibungIn this seminar participating students review, present, and discuss (mostly recent) research papers in the area of computer networks. This semester the seminar will focus on topics blending networks with machine learning and control theory.
LernzielThe two main goals of this seminar are: 1) learning how to read and review scientific papers; and 2) learning how to present and discuss technical topics with an audience of peers.

Students are required to attend the entire seminar, choose a paper to present from a given list, prepare and give a presentation on that topic, and lead the follow-up discussion. To ensure the talks' quality, each student will be mentored by a teaching assistant. In addition to presenting one paper, every student is also required to submit one (short) review for one of the two papers presented every week in-class (12 reviews in total).

The students will be evaluated based on their submitted reviews, their presentation, their leadership in animating the discussion for their own paper, and their participation in the discussions of other papers.
InhaltThe seminar will start with two introductory lectures in week 1 and week 2. Starting from week 3, participating students will start reviewing, presenting, and discussing research papers. Each week will see two presentations, for a total of 24 papers.

The course content will vary from semester to semester. This semester, the seminar will focus on topics blending networks with machine learning and control theory. For details, please see: https://seminar-net.ethz.ch
SkriptThe slides of each presentation will be made available on the website.
LiteraturThe paper selection will be made available on the course website: https://seminar-net.ethz.ch
Voraussetzungen / BesonderesCommunication Networks (227-0120-00L) or equivalents. It is expected that students have prior knowledge in machine learning and control theory, for instance by having attended appropriate courses.