Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Informatik Master
Vertiefungsfächer
Vertiefung General Studies
Seminar in General Studies
252-3002-00L	Algorithms for Database Systems Number of participants limited to 15. 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.	W	2 KP	2S	P. Penna
Kurzbeschreibung	Query processing, optimization, stream-based systems, distributed and parallel databases, non-standard databases.
Lernziel	Develop an understanding of selected problems of current interest in the area of algorithms for database systems.
252-4102-00L	Seminar on Randomized Algorithms and Probabilistic Methods 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. Number of participants limited to 24.	W	2 KP	2S	A. Steger
Kurzbeschreibung	The aim of the seminar is to study papers which bring the students to the forefront of today's research topics. This semester we will study selected papers of the conference Symposium on Discrete Algorithms (SODA18).
Lernziel	Read papers from the forefront of today's research; learn how to give a scientific talk.
Voraussetzungen / Besonderes	The seminar is open for both students from mathematics and students from computer science. As prerequisite we require that you passed the course Randomized Algorithms and Probabilistic Methods (or equivalent, if you come from abroad).
252-5704-00L	Advanced Methods in Computer Graphics 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.	W	2 KP	2S	O. Sorkine Hornung
Kurzbeschreibung	This seminar covers advanced topics in computer graphics with a focus on the latest research results. Topics include modeling, rendering, visualization, animation, physical simulation, computational photography, and others.
Lernziel	The goal is to obtain an in-depth understanding of actual problems and research topics in the field of computer graphics as well as improve presentation and critical analysis skills.
261-5113-00L	Computational Challenges in Medical Genomics Number of participants limited to 20.	W	2 KP	2S	A. Kahles, G. Rätsch
Kurzbeschreibung	This seminar discusses recent relevant contributions to the fields of computational genomics, algorithmic bioinformatics, statistical genetics and related areas. Each participant will hold a presentation and lead the subsequent discussion.
Lernziel	Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to succesfully present the key points of existing research work are the same as needed to communicate own research ideas. In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class.
Inhalt	The topics covered in the seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc. Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are amongst the list of selected papers.
Voraussetzungen / Besonderes	Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine.
263-3712-00L	Seminar on Computational Interaction 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.	W	2 KP	2S	O. Hilliges
Kurzbeschreibung	Computational Interaction focuses on the use of algorithms to enhance the interaction with a computing system. Papers from scientific venues such as CHI, UIST & SIGGRAPH will be examined in-depth. Student present and discuss the papers to extract techniques and insights that can be applied to software & hardware projects. Topics include user modeling, computational design, and input & output.
Lernziel	The 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.
Inhalt	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 (e.g. "presenter", "historian", "student", etc). The seminar will cover multiple topics of computational interaction, including: 1) User- and context modeling for UI adaptation Intent modeling, activity and emotion recognition, and user perception. 2) Computational design Design mining, design exploration, UI optimization. 3) Computer supported input Text entry, pointing, gestural input, physiological sensing, eye tracking, and sketching. 4) Computer supported output Information retrieval, fabrication, mixed reality interfaces, haptics, and gaze contingency 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. Student will learn how to incorporate computational methods into system that involve software, hardware, and, very importantly, users. Seminar website: https://ait.ethz.ch/teaching/courses/2020-SS-Seminar-Computational-Interaction/
263-4203-00L	Geometry: Combinatorics and Algorithms 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.	W	2 KP	2S	B. Gärtner, M. Hoffmann, E. Welzl, M. Wettstein
Kurzbeschreibung	This seminar complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent.
Lernziel	Each student is expected to read, understand, and elaborate on a selected research paper. To this end, (s)he should give a 45-min. presentation about the paper. The process includes * getting an overview of the related literature; * understanding and working out the background/motivation: why and where are the questions addressed relevant? * understanding the contents of the paper in all details; * selecting parts suitable for the presentation; * presenting the selected parts in such a way that an audience with some basic background in geometry and graph theory can easily understand and appreciate it.
Inhalt	This seminar is held once a year and complements the course Geometry: Combinatorics & Algorithms. Students of the seminar will present original research papers, some classic and some of them very recent. The seminar is a good preparation for a master, diploma, or semester thesis in the area.
Voraussetzungen / Besonderes	Prerequisite: Successful participation in the course "Geometry: Combinatorics & Algorithms" (takes place every HS) is required.
263-2100-00L	Research Topics in Software Engineering 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.	W	2 KP	2S	Z. Su, P. He, M. Rigger, T. Su
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.
Literatur	The publications to be presented will be announced on the seminar home page at least one week before the first session.
Voraussetzungen / Besonderes	Papers will be distributed during the first lecture.
263-2211-00L	Seminar in Computer Architecture 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.	W	2 KP	2S	O. Mutlu, M. H. K. Alser, J. Gómez Luna
Kurzbeschreibung	This 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.
Lernziel	The 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.
Inhalt	Topics 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.
Skript	All materials will be posted on the course website: https://safari.ethz.ch/architecture_seminar/ Past course materials, including the synthesis report assignment, can be found in the Fall 2019 website for the course: https://safari.ethz.ch/architecture_seminar/fall2019/doku.php
Literatur	Key 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.
Voraussetzungen / Besonderes	Design of Digital Circuits. Students should (1) have done very well in Design of Digital Circuits and (2) show a genuine interest in Computer Architecture.
263-2926-00L	Deep Learning for Big Code 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.	W	2 KP	2S	V. Raychev
Kurzbeschreibung	The 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.
Lernziel	The 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.
Inhalt	The 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 / Besonderes	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. The seminar is ideally suited for M.Sc. students in Computer Science.
263-3840-00L	Hardware Architectures for Machine Learning 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.	W	2 KP	2S	G. Alonso, T. Hoefler, C. Zhang
Kurzbeschreibung	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.
Lernziel	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.
Inhalt	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.
Voraussetzungen / Besonderes	The seminar should be of special interest to students intending to complete a master's thesis or a doctoral dissertation in related topics.
263-4651-00L	Current Topics in Cryptography 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.	W	2 KP	2S	D. Hofheinz, U. Maurer, K. Paterson
Kurzbeschreibung	In this seminar course, students present and discuss a variety of recent research papers in Cryptography.
Lernziel	Independent study of scientific literature and assessment of its contributions as well as learning and practicing presentation techniques.
Inhalt	The course lecturers will provide a list of papers from which students will select.
Literatur	The reading list will be published on the course website.
Voraussetzungen / Besonderes	Ideally, students will have taken the D-INFK Bachelors course “Information Security" or an equivalent course at Bachelors level. Ideally, they will have attended or will attend in parallel the Masters course in "Applied Cryptography”.
263-5225-00L	Advanced Topics in Machine Learning and Data Science Number of participants limited to 20. The deadline for deregistering expires at the end of the fourth week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.	W	2 KP	2S	F. Perez Cruz
Kurzbeschreibung	In this seminar, recent papers of the machine learning and data science literature are presented and discussed. Possible topics cover statistical models, machine learning algorithms and its applications.
Lernziel	The seminar “Advanced Topics in Machine Learning and Data Science” familiarizes students with recent developments in machine learning and data science. Recently published articles, as well as influential papers, have to be presented and critically reviewed. The students will learn how to structure a scientific presentation, which covers the motivation, key ideas and main results of a scientific paper. An important goal of the seminar presentation is to summarize the essential ideas of the paper in sufficient depth for the audience to be able to follow its main conclusion, especially why the article is (or is not) worth attention. 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 machine learning and data science literatures. The topics will vary from year to year but they are centered on methodological issues in machine learning and its application, not only to text or images, but other scientific domains like medicine, climate or physics.
Literatur	The papers will be presented in the first session of the seminar.
263-5904-00L	Deep Learning for Computer Vision: Seminal Work 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.	W	2 KP	2S	M. R. Oswald, Z. Cui
Kurzbeschreibung	This seminar covers seminal papers on the topic of deep learning for computer vision. The students will present and discuss the papers and gain an understanding of the most influential research in this area - both past and present.
Lernziel	The objectives of this seminar are two-fold. Firstly, the aim is to provide a solid understanding of key contributions to the field of deep learning for vision (including a historical perspective as well as recent work). Secondly, the students will learn to critically read and analyse original research papers and judge their impact, as well as how to give a scientific presentation and lead a discussion on their topic.
Inhalt	The seminar will start with introductory lectures to provide (1) a compact overview of challenges and relevant machine learning and deep learning research, and (2) a tutorial on critical analysis and presentation of research papers. Each student then chooses one paper from the provided collection to present during the remainder of the seminar. The students will be supported in the preparation of their presentation by the seminar assistants.
Skript	The selection of research papers will be presented at the beginning of the semester.
Literatur	The course "Machine Learning" is recommended.
227-0559-00L	Seminar in Deep Reinforcement Learning Number of participants limited to 25.	W	2 KP	2S	R. Wattenhofer, O. Richter
Kurzbeschreibung	In this seminar participating students present and discuss recent research papers in the area of deep reinforcement learning. The seminar starts with two introductory lessons introducing the basic concepts. Alongside the seminar a programming challenge is posed in which students can take part to improve their grade.
Lernziel	Since Google Deepmind presented the Deep Q-Network (DQN) algorithm in 2015 that could play Atari-2600 games at a superhuman level, the field of deep reinforcement learning gained a lot of traction. It sparked media attention with AlphaGo and AlphaZero and is one of the most prominent research areas. Yet many research papers in the area come from one of two sources: Google Deepmind or OpenAI. In this seminar we 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 reinforcement learning.
Inhalt	Two introductory courses introducing Q-learning and policy gradient methods. Afterwards participating students present recent papers. For details see: www.disco.ethz.ch/courses.html
Skript	Slides of presentations will be made available.
Literatur	OpenAI course (https://spinningup.openai.com/en/latest/) plus selected papers. The paper selection can be found on www.disco.ethz.ch/courses.html.
Voraussetzungen / Besonderes	It is expected that student have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses.
227-0559-10L	Seminar in Communication Networks: Learning, Reasoning and Control Findet dieses Semester nicht statt. Number of participants limited to 24.	W	2 KP	2S	L. Vanbever, A. Singla
Kurzbeschreibung	In this seminar participating students review, present, and discuss (mostly recent) research papers in the area of computer networks. This semester the seminar will focus on topics blending networks with machine learning and control theory.
Lernziel	The 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 one of the two papers presented every week in-class (12 reviews in total). 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.
Inhalt	The seminar will start with two introductory lectures in week 1 and week 2. Starting from week 3, participating students will start reviewing, presenting, and discussing research papers. Each week will see two presentations, for a total of 24 papers. The course content will vary from semester to semester. This semester, the seminar will focus on topics blending networks with machine learning and control theory. For details, please see: https://seminar-net.ethz.ch
Skript	The slides of each presentation will be made available on the website.
Literatur	The paper selection will be made available on the course website: https://seminar-net.ethz.ch
Voraussetzungen / Besonderes	Communication Networks (227-0120-00L) or equivalents. It is expected that students have prior knowledge in machine learning and control theory, for instance by having attended appropriate courses.
227-0126-00L	Advanced Topics in Networked Embedded Systems	W	2 KP	1S	L. Thiele, J. Beutel
Kurzbeschreibung	The 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.
Lernziel	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. In addition, participants will improve their presentation, reading and reviewing skills.
Inhalt	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. 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.
851-0740-00L	Big Data, Law, and Policy Number of participants limited to 35 Students will be informed by 1.3.2020 at the latest.	W	3 KP	2S	S. Bechtold
Kurzbeschreibung	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.
Lernziel	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.

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