Suchergebnis: Katalogdaten im Herbstsemester 2020

Elektrotechnik und Informationstechnologie Master Information
Master-Studium (Studienreglement 2008)
Fächer der Vertiefung
Insgesamt 42 KP müssen im Masterstudium aus Vertiefungsfächern erreicht werden. Der individuelle Studienplan unterliegt der Zustimmung eines Tutors.
Signal Processing and Machine Learning
Empfohlene Fächer
NummerTitelTypECTSUmfangDozierende
227-0225-00LLinear System TheoryW6 KP5GM. Colombino
KurzbeschreibungThe class is intended to provide a comprehensive overview of the theory of linear dynamical systems, stability analysis, and their use in control and estimation. The focus is on the mathematics behind the physical properties of these systems and on understanding and constructing proofs of properties of linear control systems.
LernzielStudents should be able to apply the fundamental results in linear system theory to analyze and control linear dynamical systems.
Inhalt- Proof techniques and practices.
- Linear spaces, normed linear spaces and Hilbert spaces.
- Ordinary differential equations, existence and uniqueness of solutions.
- Continuous and discrete-time, time-varying linear systems. Time domain solutions. Time invariant systems treated as a special case.
- Controllability and observability, duality. Time invariant systems treated as a special case.
- Stability and stabilization, observers, state and output feedback, separation principle.
SkriptAvailable on the course Moodle platform.
Voraussetzungen / BesonderesSufficient mathematical maturity, in particular in linear algebra, analysis.
227-0417-00LInformation Theory IW6 KP4GA. Lapidoth
KurzbeschreibungThis course covers the basic concepts of information theory and of communication theory. Topics covered include the entropy rate of a source, mutual information, typical sequences, the asymptotic equi-partition property, Huffman coding, channel capacity, the channel coding theorem, the source-channel separation theorem, and feedback capacity.
LernzielThe fundamentals of Information Theory including Shannon's source coding and channel coding theorems
InhaltThe entropy rate of a source, Typical sequences, the asymptotic equi-partition property, the source coding theorem, Huffman coding, Arithmetic coding, channel capacity, the channel coding theorem, the source-channel separation theorem, feedback capacity
LiteraturT.M. Cover and J. Thomas, Elements of Information Theory (second edition)
227-0421-00LLearning in Deep Artificial and Biological Neuronal NetworksW4 KP3GB. Grewe
KurzbeschreibungDeep-Learning (DL) a brain-inspired weak for of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. However, DL is far from being understood and investigating learning in biological networks might serve again as a compelling inspiration to think differently about state-of-the-art ANN training methods.
LernzielThe main goal of this lecture is to provide a comprehensive overview into the learning principles neuronal networks as well as to introduce a diverse skill set (e.g. simulating a spiking neuronal network) that is required to understand learning in large, hierarchical neuronal networks. To achieve this the lectures and exercises will merge ideas, concepts and methods from machine learning and neuroscience. These will include training basic ANNs, simulating spiking neuronal networks as well as being able to read and understand the main ideas presented in today’s neuroscience papers.
After this course students will be able to:
- read and understand the main ideas and methods that are presented in today’s neuroscience papers
- explain the basic ideas and concepts of plasticity in the mammalian brain
- implement alternative ANN learning algorithms to ‘error backpropagation’ in order to train deep neuronal networks.
- use a diverse set of ANN regularization methods to improve learning
- simulate spiking neuronal networks that learn simple (e.g. digit classification) tasks in a supervised manner.
InhaltDeep-learning a brain-inspired weak form of AI allows training of large artificial neuronal networks (ANNs) that, like humans, can learn real-world tasks such as recognizing objects in images. The origins of deep hierarchical learning can be traced back to early neuroscience research by Hubel and Wiesel in the 1960s, who first described the neuronal processing of visual inputs in the mammalian neocortex. Similar to their neocortical counterparts ANNs seem to learn by interpreting and structuring the data provided by the external world. However, while on specific tasks such as playing (video) games deep ANNs outperform humans (Minh et al, 2015, Silver et al., 2018), ANNs are still not performing on par when it comes to recognizing actions in movie data and their ability to act as generalizable problem solvers is still far behind of what the human brain seems to achieve effortlessly. Moreover, biological neuronal networks can learn far more effectively with fewer training examples, they achieve a much higher performance in recognizing complex patterns in time series data (e.g. recognizing actions in movies), they dynamically adapt to new tasks without losing performance and they achieve unmatched performance to detect and integrate out-of-domain data examples (data they have not been trained with). In other words, many of the big challenges and unknowns that have emerged in the field of deep learning over the last years are already mastered exceptionally well by biological neuronal networks in our brain. On the other hand, many facets of typical ANN design and training algorithms seem biologically implausible, such as the non-local weight updates, discrete processing of time, and scalar communication between neurons. Recent evidence suggests that learning in biological systems is the result of the complex interplay of diverse error feedback signaling processes acting at multiple scales, ranging from single synapses to entire networks.
SkriptThe lecture slides will be provided as a PDF after each lecture.
Voraussetzungen / BesonderesThis advanced level lecture requires some basic background in machine/deep learning. Thus, students are expected to have a basic mathematical foundation, including linear algebra, multivariate calculus, and probability. The course is not to be meant as an extended tutorial of how to train deep networks in PyTorch or Tensorflow, although these tools used.
The participation in the course is subject to the following conditions:

1) The number of participants is limited to 120 students (MSc and PhDs).

2) Students must have taken the exam in Deep Learning (263-3210-00L) or have acquired equivalent knowledge.
227-0445-10LMathematical Methods of Signal Processing Information W6 KP4GH. G. Feichtinger
KurzbeschreibungThis course offers a mathematical correct but still non-technical description of key objects relevant for signal processing, such as Dirac
measures, Dirac combs, various function spaces (like L^2), impulse response, transfer function, Gabor expansion, and so on. The approach is based on properties of "Feichtinger's algebra". MATLAB routines will serve as illustration.
LernzielThe aim of the class to familiarize the participants with the idea of generalized functions (usual called distributions), and to provide a (novel approach) to a theory of mild distributions, which cannot be found in books so far (the course will contribute to the development of such a book). From the physical point of view, such an object is something, which can be measured or captured by (linear) measurements, such as an audio signal. The Harmonic Analysis perspective is, that the Fourier transform and time-frequency transforms are possible over any locally compact group. Engineers talk about discrete or continuous, periodic and non-periodic signals. Hence, a unified approach to these settings and a discussion of their interconnection (e.g. approximately computing the Fourier transform of a function using the DFT) is at the heart of this course.
InhaltMathematical Foundations of Signal Processing:

0. Recalling (on and off) concepts from linear algebra (e.g. linear mappings, etc.) and introducing concepts from basic linear functional analysis (Hilbert spaces, Banach spaces)

1. Translation invariant systems and convolution, elementary functional analytic approach;

2. Pure frequencies and the Fourier transform, convolution theorem

3. The subalgebra L1(Rd) of integrable functions (without Lebesgue integration), Riemann Lebesgue Lemma

4. Plancherels Theorem, L2(Rd) and basic Hilbert space theory, unitary mappings

5. Short-time Fourier transform, the Feichtinger algebra S0(Rd) as algebra of test functions

6. The dual space of mild distributions, relationship to tempered distributions (for this familiar); various characterization

7. Gabor expansions of signals, characterization of smoothness and decay, Gabor frames and Riesz bases;

8. Transition from continuous to discrete variables, from periodic to the non-periodic case;

9. The kernel theorem, as the continuous analogue of matrix representations;

10. Sobolev spaces (describing smoothness) and weighted spaces;

11. Spreading representation and Kohn-Nirenberg representation of operators;

12. Gabor multipliers and approximation of slowly varying systems;

13. As time permits: the idea of generalized stochastic processes

14. Further subjects as demanded by the audience can be covered on demand.


Detailed lecture notes will be provided. This material will become part of an on-going book-project, which has many facets.
SkriptThis material will be regularly updated and posted at the lecturer's homepage, at Link

There will be also a dedicated WEB page at Link (to be installed in the near future).
Voraussetzungen / BesonderesWe encourage students who are interested in mathematics, but also students of physics or mathematics who want to learn about application of modern methods from functional analysis to their sciences, especially those who are interested to understand what the connections between the continuous and the discrete world are (from continuous functions or images to samples or pixels, and back).

Hans G. Feichtinger (Link)

For any kind of questions concerning this course please contact the lecturer. He will be in Zurich most of the time, even if the course has to be held offline. It will start by October 1st 2020 only.
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
263-5210-00LProbabilistic Artificial Intelligence Information Belegung eingeschränkt - Details anzeigen W8 KP3V + 2U + 2AA. Krause
KurzbeschreibungThis course introduces core modeling techniques and algorithms from machine learning, optimization and control for reasoning and decision making under uncertainty, and study applications in areas such as robotics and the Internet.
LernzielHow can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for graduate students.
InhaltTopics covered:
- Probability
- Probabilistic inference (variational inference, MCMC)
- Bayesian learning (Gaussian processes, Bayesian deep learning)
- Probabilistic planning (MDPs, POMPDPs)
- Multi-armed bandits and Bayesian optimization
- Reinforcement learning
Voraussetzungen / BesonderesSolid basic knowledge in statistics, algorithms and programming.
The material covered in the course "Introduction to Machine Learning" is considered as a prerequisite.
401-0647-00LIntroduction to Mathematical Optimization Belegung eingeschränkt - Details anzeigen W5 KP2V + 1UD. Adjiashvili
KurzbeschreibungIntroduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.
LernzielThe goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering.
InhaltTopics covered in this course include:
- Linear programming (simplex method, duality theory, shadow prices, ...).
- Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...).
- Modelling with mathematical optimization: applications of mathematical programming in engineering.
LiteraturInformation about relevant literature will be given in the lecture.
Voraussetzungen / BesonderesThis course is meant for students who did not already attend the course "Mathematical Optimization", which is a more advance lecture covering similar topics. Compared to "Mathematical Optimization", this course has a stronger focus on modeling and applications.
401-3054-14LProbabilistic Methods in Combinatorics Information W6 KP2V + 1UB. Sudakov
KurzbeschreibungThis course provides a gentle introduction to the Probabilistic Method, with an emphasis on methodology. We will try to illustrate the main ideas by showing the application of probabilistic reasoning to various combinatorial problems.
Lernziel
InhaltThe topics covered in the class will include (but are not limited to): linearity of expectation, the second moment method, the local lemma, correlation inequalities, martingales, large deviation inequalities, Janson and Talagrand inequalities and pseudo-randomness.
Literatur- The Probabilistic Method, by N. Alon and J. H. Spencer, 3rd Edition, Wiley, 2008.
- Random Graphs, by B. Bollobás, 2nd Edition, Cambridge University Press, 2001.
- Random Graphs, by S. Janson, T. Luczak and A. Rucinski, Wiley, 2000.
- Graph Coloring and the Probabilistic Method, by M. Molloy and B. Reed, Springer, 2002.
401-3621-00LFundamentals of Mathematical Statistics Information W10 KP4V + 1US. van de Geer
KurzbeschreibungThe course covers the basics of inferential statistics.
Lernziel
401-3901-00LMathematical OptimizationW11 KP4V + 2UR. Zenklusen
KurzbeschreibungMathematical treatment of diverse optimization techniques.
LernzielThe goal of this course is to get a thorough understanding of various classical mathematical optimization techniques with an emphasis on polyhedral approaches. In particular, we want students to develop a good understanding of some important problem classes in the field, of structural mathematical results linked to these problems, and of solution approaches based on this structural understanding.
InhaltKey topics include:
- Linear programming and polyhedra;
- Flows and cuts;
- Combinatorial optimization problems and techniques;
- Equivalence between optimization and separation;
- Brief introduction to Integer Programming.
Literatur- Bernhard Korte, Jens Vygen: Combinatorial Optimization. 6th edition, Springer, 2018.
- Alexander Schrijver: Combinatorial Optimization: Polyhedra and Efficiency. Springer, 2003. This work has 3 volumes.
- Ravindra K. Ahuja, Thomas L. Magnanti, James B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.
- Alexander Schrijver: Theory of Linear and Integer Programming. John Wiley, 1986.
Voraussetzungen / BesonderesSolid background in linear algebra.
401-4619-67LAdvanced Topics in Computational Statistics
Findet dieses Semester nicht statt.
W4 KP2Vkeine Angaben
KurzbeschreibungThis lecture covers selected advanced topics in computational statistics. This year the focus will be on graphical modelling.
LernzielStudents learn the theoretical foundations of the selected methods, as well as practical skills to apply these methods and to interpret their outcomes.
InhaltThe main focus will be on graphical models in various forms:
Markov properties of undirected graphs; Belief propagation; Hidden Markov Models; Structure estimation and parameter estimation; inference for high-dimensional data; causal graphical models
Voraussetzungen / BesonderesWe assume a solid background in mathematics, an introductory lecture in probability and statistics, and at least one more advanced course in statistics.
Fächer von allgemeinem Interesse
Diese Fächer sind für mehrere Vertiefungsrichtungen wählbar. Sprechen Sie mit Ihrem Tutor.
NummerTitelTypECTSUmfangDozierende
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
363-0790-00LTechnology Entrepreneurship Information W2 KP2VF. Hacklin
KurzbeschreibungThis course aims to equip future leaders with strategies, frameworks and tools for understanding, analyzing and building technology ventures. In so doing, this course lays particular emphasis on providing an overview of various technology-related dimensions of the entrepreneurial journey, including founding, financing and growing a venture.
Lernziel- Understand both the tension and link between entrepreneurship and technology
- Evaluate cases of success and failure in technology ventures
- Discuss a variety of approaches and frameworks for building and growing technology ventures
- Interact with entrepreneurial leaders and gain insight into their entrepreneurial journey
- Experiment with building blocks and tools for analyzing, structuring and prototyping technology ventures
InhaltMany industries are approaching, or find themselves in the midst of, dramatic structural changes. In many cases, such transformations are rooted in underlying technological shifts, such as digitization, nanoscale engineering, or 3D printing. Well known cases in point of affected sectors are in consumer electronics, media or manufacturing industries who are currently undergoing significant technology-driven disruptions. But also emerging shifts in the automotive sector or financial services give rise to severe questions of where and how the future value will be created and captured.
In a world characterized by disruption and change, technology ventures have taken a paramount role in significantly altering the global economic picture. As a consequence, there is a rising demand for complementing technological skills by entrepreneurial understanding.
Against this background, this course aims to equip future leaders with strategies, frameworks and tools for understanding, analyzing and building technology ventures. In so doing, this course lays particular emphasis on providing an overview of various technology-related dimensions of the entrepreneurial journey, including founding, financing and growing a venture.

See course website: Link
Skript- Lecture slides, cases and additional learning material provided during the course
151-0317-00LVisualization, Simulation and Interaction - Virtual Reality IIW4 KP3GA. Kunz
KurzbeschreibungThis lecture provides deeper knowledge on the possible applications of virtual reality, its basic technolgy, and future research fields. The goal is to provide a strong knowledge on Virtual Reality for a possible future use in business processes.
LernzielVirtual Reality can not only be used for the visualization of 3D objects, but also offers a wide application field for small and medium enterprises (SME). This could be for instance an enabling technolgy for net-based collaboration, the transmission of images and other data, the interaction of the human user with the digital environment, or the use of augmented reality systems.
The goal of the lecture is to provide a deeper knowledge of today's VR environments that are used in business processes. The technical background, the algorithms, and the applied methods are explained more in detail. Finally, future tasks of VR will be discussed and an outlook on ongoing international research is given.
InhaltIntroduction into Virtual Reality; basisc of augmented reality; interaction with digital data, tangible user interfaces (TUI); basics of simulation; compression procedures of image-, audio-, and video signals; new materials for force feedback devices; intorduction into data security; cryptography; definition of free-form surfaces; digital factory; new research fields of virtual reality
SkriptThe handout is available in German and English.
Voraussetzungen / BesonderesPrerequisites:
"Visualization, Simulation and Interaction - Virtual Reality I" is recommended, but not mandatory.

Didactical concept:
The course consists of lectures and exercises.
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