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Suchergebnis: Katalogdaten im Herbstsemester 2018

Informatik Master Information
Vertiefungsfächer
Vertiefung General Studies
Seminar in General Studies
NummerTitelTypECTSUmfangDozierende
263-2926-00LDeep Learning for Big Code Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 24.

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 KP2SV. Raychev
KurzbeschreibungThe seminar covers some of the latest and most exciting developments (industrial and research) in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities.
LernzielThe objective of the seminar is to:

- Introduce students to the field of Deep Learning for Big Code.

- Learn how machine learning models can be used to solve practical challenges in software engineering and programming beyond traditional methods.

- Highlight the latest research and work opportunities in industry and academia available on this topic.
InhaltThe last 5 years have seen increased interest in applying advanced machine learning techniques such as deep learning to new kind of data: program code. As the size of open source code increases dramatically (over 980 billion lines of code written by humans), so comes the opportunity for new kind of deep probabilistic methods and commercial systems that leverage this data to revolutionize software creation and address hard problems not previously possible. Examples include: machines writing code, program de-obfuscation for security, code search, and many more.

Interestingly, this new type of data, unlike natural language and images, introduces technical challenges not typically encountered when working with standard datasets (e.g., images, videos, natural language), for instance, finding the right representation over which deep learning operates. This in turn has the potential to drive new kinds of machine learning models with broad applicability.

Because of this, there has been substantial interest over the last few years in both industry (e.g., companies such as Facebook starting, various start-ups in the space such as http://deepcode.ai), academia (e.g., http://plml.ethz.ch) and government agencies (e.g., DARPA) on using machine learning to automate various programming tasks.

In this seminar, we will cover some of the latest and most exciting developments in the field of Deep Learning for Code, including new methods and latest systems, as well as open challenges and opportunities.

The seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation.
Voraussetzungen / BesonderesThe seminar is carried out as a set of presentations chosen from a list of available papers. The grade is determined as a function of the presentation, handling questions and answers, and participation.

The seminar is ideally suited for M.Sc. students in Computer Science.
263-2930-00LBlockchain Security Seminar Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 26.

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 KP2SP. Tsankov
KurzbeschreibungThis seminar introduces students to the latest research trends in the field of blockchains.
LernzielThe objectives of this seminar are twofold: (1) learning about the blockchain platform, a prominent technology receiving a lot of attention in computer Science and economy and (2) learning to convey and present complex and technical concepts in simple terms, and in particular identifying the core idea underlying the technicalities.
InhaltThis seminar introduces students to the latest research trends in the field of blockchains. The seminar covers the basics of blockchain technology, including motivation for decentralized currency, establishing trust between multiple parties using consensus algorithms, and smart contracts as a means to establish decentralized computation. It also covers security issues arising in blockchains and smart contracts as well as automated techniques for detecting vulnerabilities using programming language techniques.
263-3504-00LHardware Acceleration for Data Processing 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 KP2SG. Alonso, T. Hoefler, C. Zhang
KurzbeschreibungThe seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.
LernzielThe seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.
InhaltThe general application areas are big data and machine learning. The systems covered will include systems from computer architecture, high performance computing, data appliances, and data centers.
Voraussetzungen / BesonderesStudents taking this seminar should have the necessary background in systems and low level programming.
263-3900-00LCommunication Networks Seminar Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 20.

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 KP2SA. Singla
KurzbeschreibungWe explore recent advances in networking by reading high quality research papers, and discussing open research opportunities, most of which are suitable for students to later take up as thesis or semester projects.
LernzielThe objectives are (a) to understand the state-of-the-art in the field; (b) to learn to read, present and critique papers; (c) to engage in discussion and debate about research questions; and (d) to identify opportunities for new research.

Students are expected to attend the entire seminar, choose a topic for presentation from a given list, make a presentation on that topic, and lead the discussion. Further, for each reading, every student needs to submit a review before the in-class discussion. Students are evaluated on their submitted reviews, their presentation and discussion leadership, and participation in seminar discussions.
LiteraturA program will be posted here: https://ndal.ethz.ch/courses/networks-seminar.html, comprising of a list of papers the seminar group will cover.
Voraussetzungen / BesonderesAn undergraduate-level understanding of networking, such that the student is familiar with concepts like reliable transport protocols (like TCP) and basics of Internet routing. ETH courses that fulfill this requirement: Computer Networks (252-0064-00L) and its predecessor (Operating Systems and Networks -- 252-0062-00L). Similar courses at other universities are also sufficient.
263-4505-00LAlgorithms for Large-Scale Graph Processing 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 KP2SM. Ghaffari
KurzbeschreibungThis is a theory seminar, where we present and discuss recent algorithmic developments for
processing large-scale graphs. In particular, we focus on Massively Parallel Computation (MPC)
algorithms. MPC is a clean and general theoretical framework that captures the essential aspects
of computational problems in large-scale processing settings such as MapReduce, Hadoop,
Spark, Dryad, etc.
LernzielThis seminar familiarizes students with foundational aspects of large-scale graph processing,
and especially the related algorithmic tools and techniques. In particular, we discuss recent
developments in the area of Massively Parallel Computation. This is a mathematical abstraction
of practical large-scale processing settings such as MapReduce, and it has been receiving
significant attention over the past few years.

The seminar assumes no particular familiarity with parallel computation. However, we expect
that all the students are comfortable with basics of algorithms design and analysis, as well as
probability theory.

In the course of the seminar, the students learn how to structure a scientific presentation
(in English) which covers the key ideas of a paper, while omitting the less significant details.
InhaltThe seminar will cover a number of the recent papers on Massively Parallel Computation.
As mentioned above, no familiarity with parallel computation is needed and all the relevant
background information will be explain by the instructor in the first lecture.
LiteraturThe papers will be presented in the first session of the seminar.
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