Search result: Catalogue data in Spring Semester 2018
Computer Science Master | ||||||
Focus Courses | ||||||
Focus Courses in Distributed Systems | ||||||
Seminar in Distributed Systems | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|---|
252-3600-02L | Smart Systems Seminar | W | 2 credits | 2S | O. Hilliges, S. Coros, F. Mattern | |
Abstract | Seminar on various topics from the broader areas of Ubiquitous Computing, Human Computer Interaction, Robotics and Digital Fabrication. | |||||
Objective | Learn about various current topics from the broader areas of Ubiquitous Computing, Human Computer Interaction, Robotics and Digital Fabrication. | |||||
Prerequisites / Notice | There 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. | |||||
263-3830-00L | Software Defined Networking: The Data Centre Perspective | W | 2 credits | 2S | T. Roscoe, D. Wagenknecht-Dimitrova | |
Abstract | Software 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. | |||||
Objective | Through 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. | |||||
Content | Software 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. | |||||
Literature | The 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 / Notice | The seminar bases on active and interactive participation of the students. | |||||
263-3840-00L | Hardware Architectures for Machine Learning | W | 2 credits | 2S | G. Alonso, T. Hoefler, O. Mutlu, C. Zhang | |
Abstract | The 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. | |||||
Objective | The 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. | |||||
Content | The 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 / Notice | The seminar should be of special interest to students intending to complete a master's thesis or a doctoral dissertation in related topics. | |||||
263-4845-00L | Distributed Stream Processing: Systems and Algorithms Does not take place this semester. | W | 2 credits | 2S | ||
Abstract | In 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. | |||||
Objective | The 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. | |||||
Content | Modern 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) | |||||
227-0126-00L | Advanced Topics in Networked Embedded Systems Number of participants limited to 12. | W | 2 credits | 1S | L. Thiele, J. Beutel, Z. Zhou | |
Abstract | The seminar will cover advanced topics in networked embedded systems. A particular focus are cyber-physical systems and sensor networks in various application domains. | |||||
Objective | The 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. | |||||
Content | The 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. | |||||
227-0559-00L | Seminar in Distributed Computing | W | 2 credits | 2S | R. Wattenhofer | |
Abstract | In 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. | |||||
Objective | In 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 | |||||
Content | Different each year. For details see: Link | |||||
Lecture notes | Slides of presentations will be made available. | |||||
Literature | Papers. The actual paper selection can be found on Link. | |||||
851-0740-00L | Big Data, Law, and Policy Number of participants limited to 35 Students will be informed by 4.3.2018 at the latest | W | 3 credits | 2S | S. Bechtold, T. Roscoe, E. Vayena | |
Abstract | This 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. | |||||
Objective | This 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. |
- Page 1 of 1