Suchergebnis: Katalogdaten im Herbstsemester 2017
|CAS in Informatik|
|252-4202-00L||Seminar in Theoretical Computer Science||W||2 KP||2S||E. Welzl, B. Gärtner, M. Hoffmann, J. Lengler, A. Steger, B. Sudakov|
|Kurzbeschreibung||Präsentation wichtiger und aktueller Arbeiten aus der theoretischen Informatik, sowie eigener Ergebnisse von Diplomanden und Doktoranden.|
|Lernziel||Das Lernziel ist, Studierende an die aktuelle Forschung heranzuführen und sie in die Lage zu versetzen, wissenschaftliche Arbeiten zu lesen, zu verstehen, und zu präsentieren.|
|252-4601-00L||Current Topics in Information Security |
Number of participants limited to 24.
|W||2 KP||2S||D. Basin, S. Capkun, A. Perrig|
|Kurzbeschreibung||The seminar covers various topics in information security: security protocols (models, specification & verification), trust management, access control, non-interference, side-channel attacks, identity-based cryptography, host-based attack detection, anomaly detection in backbone networks, key-management for sensor networks.|
|Lernziel||The main goals of the seminar are the independent study of scientific literature and assessment of its contributions as well as learning and practicing presentation techniques.|
|Inhalt||The seminar covers various topics in information security, including network security, cryptography and security protocols. The participants are expected to read a scientific paper and present it in a 35-40 min talk. At the beginning of the semester a short introduction to presentation techniques will be given.|
- security protocols: models, specification & verification
- trust management, access control and non-interference
- side-channel attacks
- identity-based cryptography
- host-based attack detection
- anomaly detection in backbone networks
- key-management for sensor networks
|Literatur||The reading list will be published on the course web site.|
|252-5051-00L||Advanced Topics in Machine Learning |
Number of participants limited to 40.
|W||2 KP||2S||J. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch|
|Kurzbeschreibung||In this seminar, recent papers of the pattern recognition and machine learning literature are presented and discussed. Possible topics cover statistical models in computer vision, graphical models and machine learning.|
|Lernziel||The seminar "Advanced Topics in Machine Learning" familiarizes students with recent developments in pattern recognition and machine learning. Original articles have to be presented and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth while omitting details which are not essential for the understanding of the work. The presentation style will play an important role and should reach the level of professional scientific presentations.|
|Inhalt||The seminar will cover a number of recent papers which have emerged as important contributions to the pattern recognition and machine learning literature. The topics will vary from year to year but they are centered on methodological issues in machine learning like new learning algorithms, ensemble methods or new statistical models for machine learning applications. Frequently, papers are selected from computer vision or bioinformatics - two fields, which relies more and more on machine learning methodology and statistical models.|
|Literatur||The papers will be presented in the first session of the seminar.|
|252-5701-00L||Advanced Topics in Computer Graphics and Vision |
Maximale Teilnehmerzahl: 24
|W||2 KP||2S||M. Gross, O. Sorkine Hornung|
|Kurzbeschreibung||This seminar covers advanced topics in computer graphics, such as modeling, rendering, animation, real-time graphics, physical simulation, and computational photography. Each time the course is offered, a collection of research papers is selected and each student presents one paper to the class and leads a discussion about the paper and related topics.|
|Lernziel||The goal is to get an in-depth understanding of actual problems and research topics in the field of computer graphics as well as improve presentations and critical analysis skills.|
|Inhalt||This seminar covers advanced topics in computer graphics,|
including both seminal research papers as well as the latest
research results. Each time the course is offered, a collection of
research papers are selected covering topics such as modeling,
rendering, animation, real-time graphics, physical simulation, and
computational photography. Each student presents one paper to the
class and leads a discussion about the paper and related topics.
All students read the papers and participate in the discussion.
|Literatur||Individual research papers are selected each term. See http://graphics.ethz.ch/ for the current list.|
|Voraussetzungen / Besonderes||Prerequisites: |
The courses "Computer Graphics I and II" (GDV I & II) are recommended, but not mandatory.
|263-2100-00L||Research Topics in Software Engineering |
Maximale Teilnehmerzahl: 22
|W||2 KP||2S||P. Müller, T. Gross, M. Püschel, M. Vechev|
|Kurzbeschreibung||This seminar is an opportunity to become familiar with current research in software engineering and more generally with the methods and challenges of scientific research.|
|Lernziel||Each student will be asked to study some papers from the recent software engineering literature and review them. This is an exercise in critical review and analysis. Active participation is required (a presentation of a paper as well as participation in discussions).|
|Inhalt||The aim of this seminar is to introduce students to recent research results in the area of programming languages and software engineering. To accomplish that, students will study and present research papers in the area as well as participate in paper discussions. The papers will span topics in both theory and practice, including papers on program verification, program analysis, testing, programming language design, and development tools. A particular focus will be on domain-specific languages.|
|Literatur||The publications to be presented will be announced on the seminar home page at least one week before the first session.|
|Voraussetzungen / Besonderes||Organizational note: the seminar will meet only when there is a scheduled presentation. Please consult the seminar's home page for information.|
|263-3504-00L||Hardware Acceleration for Data Processing||W||2 KP||2S||G. Alonso, T. Hoefler, O. Mutlu, C. Zhang|
|Kurzbeschreibung||The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.|
|Lernziel||The seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.|
|Inhalt||The 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 / Besonderes||Students taking this seminar should have the necessary background in systems and low level programming.|
|263-3900-00L||Communication Networks Seminar |
Maximale Teilnehmerzahl: 20
|W||2 KP||2S||A. Singla|
|Kurzbeschreibung||We will study recent advances in computer networking by reading, presenting, and discussing research papers from recent iterations of the top conferences in the area, including NSDI, SIGCOMM, and CoNEXT.|
|Lernziel||The 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.
|263-2920-00L||Machine Learning for Interactive Systems and Advanced Programming Tools |
Findet dieses Semester nicht statt.
|W||2 KP||2S||O. Hilliges, M. Vechev|
|Kurzbeschreibung||Seminar on the intersection of machine learning, interactive systems and advanced concepts in programming and programming tools.|
|Lernziel||The seminar will cover a variety of machine learning models and algorithms (including deep neural networks) and will discuss their applications in a diverse set of domains. Furthermore, the seminar will discuss how domain knowledge is integrated into vanilla ML models.|
|Inhalt||Seminars often suffer from poor attention retention and low student engagement. This is often due to the format of the seminar where only one student reads papers in-depth and then prepares a long presentation about one or sometimes several papers. There is little reason for the other students to really pay attention or engage in the discussion. |
To improve this the seminar 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.
The seminar is organized with each student taking one of the following roles on a rotating basis:
Conference Reviewer (e.g., reviewer of UIST/ICML/PLDI ): Complete a full critical review of the paper. Use the original review from and come to a recommendation whether the paper should be accepted or not.
Historian: Find out how this paper sits in the context of the related work. Use bibliography tools to find the most influential papers cited by this work and at least one paper influenced by the work (and summarize the two papers).
PhD student: Propose a follow-up project for your own research based on this paper - importantly the project should be directly inspired by the paper or even use/extend the method proposed.
Hacker: Implement a (simplified) version of the core aspect of the paper. Prepare a demo for the seminar. In case the complexity is too high perform an in-depth analysis of reproducibility of the paper.
Detective: Find out background information about the authors. Where did they work when the paper was published; what was their role; who else have they published with; which prior work of the authors may have inspired the current paper? Students may contact the authors (but need to adhere to politeness and courteous manners and stay on topic in their conversations).
All students (every week): Come up with alternative title; find a missing result that the paper should have included.
|Voraussetzungen / Besonderes||Participation will be limited subject to available topics.|
|263-4311-00L||Seminar on Molecular Algorithms |
Findet dieses Semester nicht statt.
Limited number of participants
|W||2 KP||2S||P. Widmayer|
|Kurzbeschreibung||Develop an understanding of selected topics in the area of molecular algorithms, and the practice of scient|
|Lernziel||Study and understanding of selected topics of interest in molecular algorithms such as: Computational Power of Molecular Algorithms, Molecular Algorithms for Solving Fundamental Tasks (Majority, Leader Election, Counting), Complexity Lower Bounds, Implementations of Algorithms in DNA.|
|Inhalt||This seminar will familiarize the students with current research on molecualr algorithms, with a focus o algorithms executable in DNA. We will have an introductory lecture covering the basics of molecular computational models, and the underlying bio-chemical phenomena. |
Subsequently, we will read and present selected reseach papers, focusing on their algorithmic content.
No prior knowledge of biology or chemistry will be required.
|Literatur||Selected research articles.|
|Voraussetzungen / Besonderes||The course will require a good understanding of Randomized Algorithms. Hence, you must have passed our "Randomized Algorithms" class (or have acquired equivalent knowledge, in exceptional cases). No prior knowledge of biology or chemistry will be assumed. The basics will be presented in an introductory lecture.|
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