Suchergebnis: Katalogdaten im Herbstsemester 2020

Informatik Master Information
Master-Studium (Studienreglement 2020)
Freie Wahlfächer (nur für Regl. 2020)
Den Studierenden steht das gesamte Lehrangebot auf Master Level im Gebiet der Informatik (oder einem verwandten Bereich) der ETH Zürich, der EPF Lausanne, der Universität Zürich und - nach vorgängiger Genehmigung durch den Studiendirektor - der übrigen Schweizer Universitäten zur individuellen Auswahl offen.
252-0293-00LWireless Networking and Mobile Computing Information W4 KP2V + 1US. Mangold
KurzbeschreibungThis course gives an overview about wireless standards and summarizes the state of art for Wi-Fi 802.11, Cellular 5G, and Internet-of-Things, including new topics such as contact tracing with Bluetooth, audio communication, cognitive radio, visible light communications. The course combines lectures with a set of assignments in which students are asked to work with a JAVA simulation tool.
LernzielThe objective of the course is to learn about the general principles of wireless communications, including physics, frequency spectrum regulation, and standards. Further, the most up-to-date standards and protocols used for wireless LAN IEEE 802.11, Wi-Fi, Internet-of-Things, sensor networks, cellular networks, visible light communication, and cognitive radios, are analyzed and evaluated. Students develop their own add-on mobile computing algorithms to improve the behavior of the systems, using a Java-based event-driven simulator. We also hand out embedded systems that can be used for experiments for optical communication.
InhaltNew: Starting 2020, we will address contact tracing, radio link budget, location distance measurements, and Bluetooth in more depth.

Wireless Communication, Wi-Fi, Contact Tracing, Bluetooth, Internet-of-Things, 5G, Standards, Regulation, Algorithms, Radio Spectrum, Cognitive Radio, Mesh Networks, Optical Communication, Visible Light Communication
SkriptThe course material will be made available by the lecturer.
Literatur(1) The course webpage (look for Stefan Mangold's site)
(2) The Java 802 protocol emulator "JEmula802" from
(3) WALKE, B. AND MANGOLD, S. AND BERLEMANN, L. (2006) IEEE 802 Wireless Systems Protocols, Multi-Hop Mesh/Relaying, Performance and Spectrum Coexistence. New York U.S.A.: John Wiley & Sons. Nov 2006.
(4) BERLEMANN, L. AND MANGOLD, S. (2009) Cognitive Radio for Dynamic Spectrum Access . New York U.S.A.: John Wiley & Sons. Jan 2009.
(5) MANGOLD, S. ET.AL. (2003) Analysis of IEEE 802.11e for QoS Support in Wireless LANs. IEEE Wireless Communications, vol 10 (6), 40-50.
Voraussetzungen / BesonderesStudents should have interest in wireless communication, and should be familiar with Java programming. Experience with GNU Octave or Matlab will help too (not required).
263-0600-00LResearch in Computer Science Belegung eingeschränkt - Details anzeigen
Nur für MSc Informatik.
W5 KP11AProfessor/innen
KurzbeschreibungSelbständige Projektarbeit unter der Leitung eines Informatik-Professors / einer Informatik-Professorin.
LernzielSelbständige Projektarbeit unter der Leitung eines Informatik-Professors / einer Informatik-Professorin.
Voraussetzungen / BesonderesNur Studierende, die eine der folgenden Bedingungen erfüllt haben, können mit einem Research Projekt beginnen:
a) 1 Lab (Interfokus Kurs) und 1 Kernfokus Kurs
b) 2 Kernfokus Kurse
c) 2 Labs (Interfokus Kurse)

Eine Aufgabenbeschreibung muss zu Beginn des Projekts beim Studiensekretariat eingereicht werden.
227-2210-00LComputer Architecture Information W8 KP6G + 1AO. Mutlu
KurzbeschreibungComputer architecture is the science & art of designing and optimizing hardware components and the hardware/software interface to create a computer that meets design goals. This course covers basic components of a modern computing system (processors, memory, interconnects, accelerators). The course takes a hardware/software cooperative approach to understanding and designing computing systems.
LernzielWe will learn the fundamental concepts of the different parts of modern computing systems, as well as the latest trends by exploring the recent research in Industry and Academia. We will extensively cover memory technologies (including DRAM and new Non-Volatile Memory technologies), memory scheduling, parallel computing systems (including multicore processors and GPUs), heterogeneous computing, processing-in-memory, interconnection networks, specialized systems for major data-intensive workloads (e.g. graph processing, bioinformatics, machine learning), etc.
InhaltThe principles presented in the lecture are reinforced in the laboratory through 1) the design and implementation of a cycle-accurate simulator, where we will explore different components of a modern computing system (e.g., pipeline, memory hierarchy, branch prediction, prefetching, caches, multithreading), and 2) the extension of state-of-the-art research simulators (e.g., Ramulator) for more in-depth understanding of specific system components (e.g., memory scheduling, prefetching).
SkriptAll the materials (including lecture slides) will be provided on the course website:
The video recordings of the lectures are expected to be made available after lectures.
LiteraturWe will provide required and recommended readings in every lecture. They will mainly consist of research papers presented in major Computer Architecture and related conferences and journals.
Voraussetzungen / BesonderesDigital Design and Computer Architecture.
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.
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