Search result: Catalogue data in Spring Semester 2020

Computer Science Master Information
Focus Courses
Focus Courses in Distributed Systems
Seminar in Distributed Systems
NumberTitleTypeECTSHoursLecturers
263-2211-00LSeminar in Computer Architecture Information Restricted registration - show details
Number of participants limited to 22.

The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
W2 credits2SO. Mutlu, M. H. K. Alser, J. Gómez Luna
AbstractThis seminar course covers fundamental and cutting-edge research papers in computer architecture. It has multiple components that are aimed at improving students' (1) technical skills in computer architecture, (2) critical thinking and analysis abilities on computer architecture concepts, as well as (3) technical presentation of concepts and papers in both spoken and written forms.
ObjectiveThe main objective is to learn how to rigorously analyze and present papers and ideas on computer architecture. We will have rigorous presentation and discussion of selected papers during lectures and a written report delivered by each student at the end of the semester.

This course is for those interested in computer architecture. Registered students are expected to attend every meeting, participate in the discussion, and create a synthesis report at the end of the course.
ContentTopics will center around computer architecture. We will, for example, discuss papers on hardware security; accelerators for key applications like machine learning, graph processing and bioinformatics; memory systems; interconnects; processing in memory; various fundamental and emerging paradigms in computer architecture; hardware/software co-design and cooperation; fault tolerance; energy efficiency; heterogeneous and parallel systems; new execution models; predictable computing, etc.
Lecture notesAll materials will be posted on the course website: https://safari.ethz.ch/architecture_seminar/
Past course materials, including the synthesis report assignment, can be found in the Fall 2019 website for the course: https://safari.ethz.ch/architecture_seminar/fall2019/doku.php
LiteratureKey papers and articles, on both fundamentals and cutting-edge topics in computer architecture will be provided and discussed. These will be posted on the course website.
Prerequisites / NoticeDesign of Digital Circuits.
Students should (1) have done very well in Design of Digital Circuits and (2) show a genuine interest in Computer Architecture.
263-3712-00LSeminar on Computational Interaction Information Restricted registration - show details
Number of participants limited to 14.

The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
W2 credits2SO. Hilliges
AbstractComputational Interaction focuses on the use of algorithms to enhance the interaction with a computing system. Papers from scientific venues such as CHI, UIST & SIGGRAPH will be examined in-depth. Student present and discuss the papers to extract techniques and insights that can be applied to software & hardware projects. Topics include user modeling, computational design, and input & output.
ObjectiveThe goal of the seminar is to familiarize students with exciting new research topics in this important area, but also to teach basic scientific writing and oral presentation skills.
ContentThe seminar will have a different structure from regular seminars to encourage more discussion and a deeper learning experience. We will use a case-study format where all students read the same paper each week but fulfill different roles and hence prepare with different viewpoints in mind (e.g. "presenter", "historian", "student", etc).

The seminar will cover multiple topics of computational interaction, including:
1) User- and context modeling for UI adaptation
Intent modeling, activity and emotion recognition, and user perception.

2) Computational design
Design mining, design exploration, UI optimization.

3) Computer supported input
Text entry, pointing, gestural input, physiological sensing, eye tracking, and sketching.

4) Computer supported output
Information retrieval, fabrication, mixed reality interfaces, haptics, and gaze contingency

For each topic, a paper will be chosen that represents the state of the art of research or seminal work that inspired and fostered future work. Student will learn how to incorporate computational methods into system that involve software, hardware, and, very importantly, users.

Seminar website: https://ait.ethz.ch/teaching/courses/2020-SS-Seminar-Computational-Interaction/
263-3840-00LHardware Architectures for Machine Learning Information
The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
W2 credits2SG. Alonso, T. Hoefler, C. Zhang
AbstractThe seminar covers recent results in the increasingly important field of hardware acceleration for data science and machine learning, both in dedicated machines or in data centers.
ObjectiveThe seminar aims at students interested in the system aspects of machine learning, who are willing to bridge the gap across traditional disciplines: machine learning, databases, systems, and computer architecture.
ContentThe seminar is intended to cover recent results in the increasingly important field of hardware acceleration for data science and machine learning, both in dedicated machines or in data centers.
Prerequisites / NoticeThe seminar should be of special interest to students intending to complete a master's thesis or a doctoral dissertation in related topics.
227-0126-00LAdvanced Topics in Networked Embedded SystemsW2 credits1SL. Thiele, J. Beutel
AbstractThe 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.
ObjectiveThe 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.
ContentThe 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.
227-0559-00LSeminar in Deep Reinforcement Learning Information Restricted registration - show details
Number of participants limited to 25.
W2 credits2SR. Wattenhofer, O. Richter
AbstractIn 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.
ObjectiveSince 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.
ContentTwo introductory courses introducing Q-learning and policy gradient methods. Afterwards participating students present recent papers. For details see: www.disco.ethz.ch/courses.html
Lecture notesSlides of presentations will be made available.
LiteratureOpenAI course (https://spinningup.openai.com/en/latest/) plus selected papers.
The paper selection can be found on www.disco.ethz.ch/courses.html.
Prerequisites / NoticeIt is expected that student have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses.
851-0740-00LBig Data, Law, and Policy Restricted registration - show details
Number of participants limited to 35

Students will be informed by 1.3.2020 at the latest.
W3 credits2SS. Bechtold
AbstractThis course introduces students to societal perspectives on the big data revolution. Discussing important contributions from machine learning and data science, the course explores their legal, economic, ethical, and political implications in the past, present, and future.
ObjectiveThis course is intended both for students of machine learning and data science who want to reflect on the societal implications of their field, and for students from other disciplines who want to explore the societal impact of data sciences. The course will first discuss some of the methodological foundations of machine learning, followed by a discussion of research papers and real-world applications where big data and societal values may clash. Potential topics include the implications of big data for privacy, liability, insurance, health systems, voting, and democratic institutions, as well as the use of predictive algorithms for price discrimination and the criminal justice system. Guest speakers, weekly readings and reaction papers ensure a lively debate among participants from various backgrounds.
227-0559-10LSeminar in Communication Networks: Learning, Reasoning and Control Restricted registration - show details
Does not take place this semester.
Number of participants limited to 24.
W2 credits2SL. Vanbever, A. Singla
AbstractIn 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.
ObjectiveThe 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.
ContentThe 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
Lecture notesThe slides of each presentation will be made available on the website.
LiteratureThe paper selection will be made available on the course website: https://seminar-net.ethz.ch
Prerequisites / NoticeCommunication 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.
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