Search result: Catalogue data in Spring Semester 2021

Computer Science Master Information
Master Studies (Programme Regulations 2009)
Focus Courses
Focus Courses in Distributed Systems
Seminar in Distributed Systems
227-2211-00LSeminar 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:
Past course materials, including the synthesis report assignment, can be found in the Fall 2020 website for the course:
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.
227-0126-00LAdvanced Topics in Networked Embedded SystemsW2 credits1SL. Thiele
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 Neural Networks 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 neural networks.
ObjectiveWe 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 neural networks. The students will learn to read, evaluate and challenge research papers, prepare coherent scientific presentations and lead a discussion on their topic.
ContentThe seminar will cover a range of research directions, with a focus on Graph Neural Networks, Algorithmic Learning, Reinforcement Learning and Natural Language Processing. It will be structured in blocks with each focus area being briefly introduced before presenting and discussing recent research papers. Papers will be allocated to the students based on their preferences.

For more information see
Lecture notesSlides of presentations will be made available.
LiteratureThe paper selection can be found on
Prerequisites / NoticeIt is expected that students have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses.
227-0559-10LSeminar in Communication Networks Information Restricted registration - show details
Number of participants limited to 24.

This lecture complements the "Communication Networks Seminar" (263-3900-01L) offered in the Autumn semester. Students can get credits either seminar, but not for both.
W2 credits2SL. Vanbever, R. Jacob
AbstractIn this seminar participating students review, present, and discuss (mostly recent) research papers in the area of computer networks.
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 the two papers presented every week in-class.

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 one introductory lecture. Starting from the second week, participating students will start reviewing, presenting, and discussing research papers. We'll discuss two papers each week.

The course content will vary from semester to semester. For details, please see:
Lecture notesThe slides of each presentation will be made available on the website.
LiteratureThe paper selection will be made available on the course website:
Prerequisites / NoticeCommunication Networks (227-0120-00L) or equivalents.
263-3712-00LAdvanced Seminar on Computational Haptics 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
AbstractHaptic rendering technologies stimulate the user’s senses of touch and motion just as felt when interacting with physical objects. Actuation techniques need to address three questions: 1) What to actuate, 2) How to actuate it and 3) When to actuate it. We will approach each of these questions from a heavily technical perspective, with a focus on optimization and machine learning to find answers.
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.
ContentHaptics rendering is the use of technology that stimulates the senses of touch and motion that would be felt by a user interacting directly with physical objects. This usually involves hardware that is capable of delivering these senses. Three questions arise here: 1) What to actuate, 2) How to actuate it and 3) When to actuate. We will approach these questions from a heavy technical perspective that usually have an optimization or machine learning focus. Papers from scientific venues such as CHI, UIST & SIGGRAPH will be examined in-depth that answer these questions (partially). Students present and discuss the papers to extract techniques and insights that can be applied to software & hardware projects. Topics revolve around computational design, sensor placement, user state interference (through machine learning), and actuation as an optimization problem.

The 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 ( "presenter", "historian", "PhD", and “Journalist”).

The final deliverables include:
20 Minute presentation as presenter
5 Minute presentation as historian
1 A4 research proposal as the PhD
1 A4 summary of the discussion as the Journalist.

Example papers are:
Tactile Rendering Based on Skin Stress Optimization - ( SIGGRAPH 2020
SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows - ( UIST 2019
Fabrication-in-the-Loop Co-Optimization of Surfaces and Styli for Drawing Haptics -( SIGGRAPH 2020

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. Students will learn how to incorporate computational methods into systems that involve software, hardware, and, very importantly, users.
LiteratureComputational Interaction, Edited by Antti Oulasvirta, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, 2018. PDF Freely available through the ETH Network.

851-0740-00LBig Data, Law, and Policy Restricted registration - show details
Number of participants limited to 35.
Students will be informed by 1.3.2021 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.
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