Search result: Catalogue data in Spring Semester 2018

Computer Science Master Information
Focus Courses
Focus Courses in Distributed Systems
Seminar in Distributed Systems
NumberTitleTypeECTSHoursLecturers
252-3600-02LSmart Systems Seminar Information W2 credits2SO. Hilliges, S. Coros, F. Mattern
AbstractSeminar on various topics from the broader areas of Ubiquitous Computing, Human Computer Interaction, Robotics and Digital Fabrication.
ObjectiveLearn about various current topics from the broader areas of Ubiquitous Computing, Human Computer Interaction, Robotics and Digital Fabrication.
Prerequisites / NoticeThere will be an orientation event several weeks before the start of the semester (possibly at the end of the preceding semester) where also first topics will be assigned to students. Please check Link for further information.
263-3830-00LSoftware Defined Networking: The Data Centre Perspective Information W2 credits2ST. Roscoe, D. Wagenknecht-Dimitrova
AbstractSoftware Defined Networks (SDN) is a change supported not only by research but also industry and redifens how traditional network management and configuration is been done.
ObjectiveThrough review and discussion of literature on an exciting new trend in networking, the students get the opportunity to get familiar with one of the most promising new developments in data centre connectivity, while at the same time they can develop soft skills related to the evaluation and presentation of professional content.
ContentSoftware Defined Networks (SDN) is a change supported not only by research but also industry and redifens how traditional network management and configuration is been done. Although much has been already investigated and there are already functional SDN-enabled switches there are many open questions ahead of the adoption of SDN inside and outside the data centre (traditional or cloud-based). With a series of seminars we will reflect on the challenges, adoption strategies and future trends of SDN to create an understanding how SDN is affecting the network operators' industry.
LiteratureThe seminar is based on recent publications by academia and industry. Links to the publications are placed on the Seminar page and can be downloaded from any location with access to the ETH campus network.
Prerequisites / NoticeThe seminar bases on active and interactive participation of the students.
263-3840-00LHardware Architectures for Machine Learning Information W2 credits2SG. Alonso, T. Hoefler, O. Mutlu, 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.
263-4845-00LDistributed Stream Processing: Systems and Algorithms Information
Does not take place this semester.
W2 credits2S
AbstractIn this seminar, we will study the design and architecture of modern distributed streaming systems as well as fundamental algorithms for analyzing data streams. We will also consider current research topics and open issues in the area of distributed stream processing.
ObjectiveThe seminar will focus on high-impact research contributions addressing open issues in the design and implementation of modern distributed stream processors. In particular, the students will read, review, present, and discuss a series of research and industrial papers.
ContentModern distributed stream processing technology enables continuous, fast, and reliable analysis of large-scale unbounded datasets. Stream processing has recently become highly popular across industry and academia due to its capabilities to both improve established data processing tasks and to facilitate novel applications with real-time requirements.

The students will read, review, present, and discuss a series of research and industrial papers covering the following topics:

- Fault-tolerance and processing guarantees
- State management
- Windowing semantics and optimizations
- Basic data stream mining algorithms (e.g. sampling, counting, filtering)
- Query languages and libraries for stream processing (e.g. Complex Event Processing, online machine learning)
227-0126-00LAdvanced Topics in Networked Embedded Systems Information Restricted registration - show details
Number of participants limited to 12.
W2 credits1SL. Thiele, J. Beutel, Z. Zhou
AbstractThe seminar will cover advanced topics in networked embedded systems. A particular focus are cyber-physical systems 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.
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.
227-0559-00LSeminar in Distributed Computing Information W2 credits2SR. Wattenhofer
AbstractIn this seminar participating students present and discuss recent research papers in the area of distributed computing. The seminar consists of algorithmic as well as systems papers in distributed computing theory, peer-to-peer computing, ad hoc and sensor networking, or multi-core computing.
ObjectiveIn the last two decades, we have experienced an unprecedented growth in the area of distributed systems and networks; distributed computing now encompasses many of the activities occurring in today's computer and communications world. This course introduces the basics of distributed computing, highlighting common themes and techniques. We study the fundamental issues underlying the design of distributed systems: communication, coordination, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques.

In this seminar, students present the latest work in this domain.

Seminar language: English
ContentDifferent each year. For details see: Link
Lecture notesSlides of presentations will be made available.
LiteraturePapers.
The actual paper selection can be found on Link.
851-0740-00LBig Data, Law, and Policy Restricted registration - show details
Number of participants limited to 35

Students will be informed by 4.3.2018 at the latest
W3 credits2SS. Bechtold, T. Roscoe, E. Vayena
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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