Search result: Catalogue data in Autumn Semester 2020

Computer Science Master Information
Master Studies (Programme Regulations 2020)
Elective Courses (only for Programme Regulations 2020)
Students can individually chose from the entire Master course offerings in the area of Computer Science (or a closely related field), from ETH Zurich, EPF Lausanne, the University of Zurich and - but only with the consent of the Director of Studies - from all other Swiss universities.
252-0293-00LWireless Networking and Mobile Computing Information W4 credits2V + 1US. Mangold
AbstractThis 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.
ObjectiveThe 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.
ContentNew: 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
Lecture notesThe course material will be made available by the lecturer.
Literature(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.
Prerequisites / NoticeStudents 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 Restricted registration - show details
Only for Computer Science MSc.
W5 credits11AProfessors
AbstractIndependent project work under the supervision of a Computer Science Professor.
ObjectiveIndependent project work under the supervision of a Computer Science Professor.
Prerequisites / NoticeOnly students who fulfill one of the following requirements are allowed to begin a research project:
a) 1 lab (interfocus course) and 1 focus course
b) 2 core focus courses
c) 2 labs (interfocus courses)

A task description must be submitted to the Student Administration Office at the beginning of the work.
227-2210-00LComputer Architecture Information W8 credits6G + 1AO. Mutlu
AbstractComputer 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.
ObjectiveWe 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.
ContentThe 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).
Lecture notesAll 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.
LiteratureWe 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.
Prerequisites / NoticeDigital Design and Computer Architecture.
227-0423-00LNeural Network Theory Information W4 credits2V + 1UH. Bölcskei
AbstractThe 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.
ObjectiveAfter 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.
Content1. 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
Lecture notesDetailed lecture notes will be provided.
Prerequisites / NoticeThis 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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