Suchergebnis: Katalogdaten im Herbstsemester 2020

Informatik Master Information
Master-Studium (Studienreglement 2009)
Vertiefungsfächer
Vertiefung General Studies
Seminar in General Studies
NummerTitelTypECTSUmfangDozierende
227-2211-00LSeminar in Computer Architecture Information Belegung eingeschränkt - Details anzeigen
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.
W2 KP2SO. Mutlu, M. H. K. Alser, J. Gómez Luna
KurzbeschreibungThis seminar course covers fundamental and cutting-edge research papers in computer architecture. It consists of 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.
LernzielThe 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.
InhaltTopics 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.
SkriptAll 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 Spring 2020 website for the course: https://safari.ethz.ch/architecture_seminar/spring2020/doku.php?id=start
LiteraturKey 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 / BesonderesDigital Design and Computer Architecture.
Students should (1) have done very well in Digital Design and Computer Architecture and (2) show a genuine interest in Computer Architecture.
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.
263-3504-00LHardware 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, A. Klimovic, 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.
263-3608-00LDigitalization and the Rebound Effect 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 KP2SV. C. Coroama
KurzbeschreibungDigitalization is hailed as a silver bullet towards environmental sustainability. Via optimizations or substitutions, it can lead to large reductions of GHG emissions and energy use. These gains, however, bear at their core the poisoned gift of rebound effects. The seminar will highlight the interplay between digitalization-induced environmental benefits and their rebound-based countereffects.
LernzielLearn about the impact of digitalization on energy consumption, greenhouse gas emissions, and environmental sustainability in general, with special emphasis on the subtler implications of rebound effects.

Learn to review scientific literature, to deliver a scientifically sound presentation respecting the allocated time, and to produce a scientific report.
InhaltIn recent years, “digitalization” became a widely discussed phenomenon in popular media. In business contexts, it now stands for the broad use of digital information and communication technology (ICT), and the subsequent induced change in business operations or whole business models (“digital transformation”). This ongoing process encompasses technological developments such as distributed sensing, ubiquitous wireless communication, the Internet of things, big data, machine learning, artificial intelligence, augmented and virtual reality, 3D printing, robotics, or automation. Through its ubiquitous and profound effects, digitalization is often restructuring or disrupting economic processes and social practices.

Given its vast capabilities, digitalization is frequently hailed as a key ingredient towards environmental sustainability. By optimizing existing processes or substituting them altogether, digitalization can lead to substantial reductions of carbon emissions as well as energy and resource use. Despite this potential, however, the sometimes spectacular efficiency gains induced by digitalization bear at their very core the poisoned gift of rebound effects. In economics, “rebound effects” are an umbrella term defining a variety of mechanisms that reduce or even overcompensate the savings from improved energy or material efficiency. In a nutshell, positive initial effects make a product more attractive (through lower prices or added benefits), which is in turn likely to spur demand for that same good or service (which became more attractive), or also for other products due to the increased disposable income or time.

This seminar will highlight selected aspects of this interplay between digitalization-induced environmental benefits and their rebound-based countereffects. The first two presentations will introduce digitalization and (the several types of) rebound effects, respectively. After analyzing the mechanisms by which digitalization can bring about environmental benefits, a couple of presentations will compare environmental chances and perils in several domains enabled or deeply affected by digitalization: teleworking, e-commerce, sharing economy (e.g. Uber, Airbnb, bicycle sharing), autonomous driving, last-minute booking, and just-in-time production.
LiteraturWill be announced at the beginning of the semester for each topic.
Voraussetzungen / BesonderesAn introduction to the seminar will be given Thursday, September 17th, 2020, during the first class. Seminar topics will be assigned to students during this session. Due to the large expected number of interested students, this first class will be held online. Please check http://vs.inf.ethz.ch/edu/HS2020/DR/ for further information’
263-3900-01LCommunication 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, L. Vanbever
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 Communication Networks (227-0120-00L). Similar courses at other universities are also sufficient.
263-5155-00LCausal Representation Learning Information Belegung eingeschränkt - Details anzeigen
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 KP2SB. Schölkopf
KurzbeschreibungDeep neural networks have achieved impressive success on prediction tasks in a supervised learning setting, provided sufficient labelled data is available. However, current AI systems lack a versatile understanding of the world around us, as shown in a limited ability to transfer and generalize between tasks.
LernzielThe goal of this class is for students to gain experience with advanced research at the intersection of causal inference and deep learning.
InhaltThe course focuses on challenges and opportunities between deep learning and causal inference, and highlights work that attempts to develop statistical representation learning towards interventional/causal world models. The course will include guest lectures from renowned scientist both from academia as well as top industrial research labs.

Deep Representation Learning, Causal Structure Learning, Disentangled Representations, Independent Mechanisms, Causal Inference, World Models and Interactive Learning.
Voraussetzungen / BesonderesBSc in Computer Science or related field (e.g. Mathematics, Physics) and passed at least one learning course e.g. Intro to Machine Learning or Probabilistic Artificial Intelligence.
Wahlfächer in der Informatik
Als Wahlfächer in der Informatik gelten alle angebotenen Kurse im Master-Studiengang des D-INFK.
NummerTitelTypECTSUmfangDozierende
252-0293-00LWireless Networking and Mobile Computing Information W4 KP2V + 1US. Mangold
KurzbeschreibungThis course gives an overview about wireless standards and summarizes the state of art for Wi-Fi 802.11, Cellular 5G, and Internet-of-Things, including new topics such as contact tracing with Bluetooth, audio communication, cognitive radio, visible light communications. The course combines lectures with a set of assignments in which students are asked to work with a JAVA simulation tool.
LernzielThe objective of the course is to learn about the general principles of wireless communications, including physics, frequency spectrum regulation, and standards. Further, the most up-to-date standards and protocols used for wireless LAN IEEE 802.11, Wi-Fi, Internet-of-Things, sensor networks, cellular networks, visible light communication, and cognitive radios, are analyzed and evaluated. Students develop their own add-on mobile computing algorithms to improve the behavior of the systems, using a Java-based event-driven simulator. We also hand out embedded systems that can be used for experiments for optical communication.
InhaltNew: Starting 2020, we will address contact tracing, radio link budget, location distance measurements, and Bluetooth in more depth.

Wireless Communication, Wi-Fi, Contact Tracing, Bluetooth, Internet-of-Things, 5G, Standards, Regulation, Algorithms, Radio Spectrum, Cognitive Radio, Mesh Networks, Optical Communication, Visible Light Communication
SkriptThe course material will be made available by the lecturer.
Literatur(1) The course webpage (look for Stefan Mangold's site)
(2) The Java 802 protocol emulator "JEmula802" from https://bitbucket.org/lfield/jemula802
(3) WALKE, B. AND MANGOLD, S. AND BERLEMANN, L. (2006) IEEE 802 Wireless Systems Protocols, Multi-Hop Mesh/Relaying, Performance and Spectrum Coexistence. New York U.S.A.: John Wiley & Sons. Nov 2006.
(4) BERLEMANN, L. AND MANGOLD, S. (2009) Cognitive Radio for Dynamic Spectrum Access . New York U.S.A.: John Wiley & Sons. Jan 2009.
(5) MANGOLD, S. ET.AL. (2003) Analysis of IEEE 802.11e for QoS Support in Wireless LANs. IEEE Wireless Communications, vol 10 (6), 40-50.
Voraussetzungen / BesonderesStudents should have interest in wireless communication, and should be familiar with Java programming. Experience with GNU Octave or Matlab will help too (not required).
263-0600-00LResearch in Computer Science Belegung eingeschränkt - Details anzeigen
Nur für MSc Informatik.
W5 KP11AProfessor/innen
KurzbeschreibungSelbständige Projektarbeit unter der Leitung eines Informatik-Professors / einer Informatik-Professorin.
LernzielSelbständige Projektarbeit unter der Leitung eines Informatik-Professors / einer Informatik-Professorin.
Voraussetzungen / BesonderesNur Studierende, die eine der folgenden Bedingungen erfüllt haben, können mit einem Research Projekt beginnen:
a) 1 Lab (Interfokus Kurs) und 1 Kernfokus Kurs
b) 2 Kernfokus Kurse
c) 2 Labs (Interfokus Kurse)

Eine Aufgabenbeschreibung muss zu Beginn des Projekts beim Studiensekretariat eingereicht werden.
227-0423-00LNeural Network Theory Information W4 KP2V + 1UH. Bölcskei
KurzbeschreibungThe class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal approximation theorems, basics of approximation theory, fundamental limits of deep neural network learning, geometry of decision surfaces, capacity of separating surfaces, dimension measures relevant for generalization, VC dimension of neural networks.
LernzielAfter attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the mathematical foundations of (deep) neural networks.
Inhalt1. Universal approximation with single- and multi-layer networks

2. Introduction to approximation theory: Fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, non-linear approximation theory

3. Fundamental limits of deep neural network learning

4. Geometry of decision surfaces

5. Separating capacity of nonlinear decision surfaces

6. Dimension measures: Pseudo-dimension, fat-shattering dimension, Vapnik-Chervonenkis (VC) dimension

7. Dimensions of neural networks

8. Generalization error in neural network learning
SkriptDetailed lecture notes will be provided.
Voraussetzungen / BesonderesThis course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular.
227-0781-00LLow-Power System Design
Findet dieses Semester nicht statt.
W6 KP2V + 2U
KurzbeschreibungIntroduction to low-power and low-energy design techniques from a systems perspective including aspects both from hard- and software. The focus of this lecture is on cutting across a number of related fields discussing architectural concepts, modeling and measurement techniques as well as software design mainly using the example of networked embedded systems.
LernzielKnowledge of the state-of-the-art in low power system design, understanding recent research results and their implication on industrial products.
InhaltDesigning systems with a low energy footprint is an increasingly important. There are many applications for low-power systems ranging from mobile devices powered from batteries such as today's smart phones to energy efficient household appliances and datacenters. Key drivers are to be found mainly in the tremendous increase of mobile devices and the growing integration density requiring to carefully reason about power, both from a provision and consumption viewpoint. Traditional circuit design classes introduce low-power solely from a hardware perspective with a focus on the power performance of a single or at most a hand full of circuit elements. Similarly, low-power aspects are touched in a multitude of other classes, mostly as a side topic. However in successfully designing systems with a low energy footprint it is not sufficient to only look at low-power as an aspect of second class. In modern low-power system design advanced CMOS circuits are of course a key ingredient but successful low-power integration involves many more disciplines such as system architecture, different sources of energy as well as storage and most importantly software and algorithms. In this lecture we will discuss aspects of low-power design as a first class citizen introducing key concepts as well as modeling and measurement techniques focusing mainly on the design of networked embedded systems but of course equally applicable to many other classes of systems. The lecture is further accompanied by a reading seminar as well as exercises and lab sessions.
SkriptExercise and lab materials, copies of lecture slides.
LiteraturA detailed reading list will be made available in the lecture.
Voraussetzungen / BesonderesKnowledge in embedded systems, system software, (wireless) networking, possibly integrated circuits, and hardware software codesign.
Industriepraktikum
NummerTitelTypECTSUmfangDozierende
252-0700-00LIndustriepraktikum Information Belegung eingeschränkt - Details anzeigen
Nur für Informatik MSc.
W0 KPexterne Veranstalter
KurzbeschreibungAn internship provides opportunities to gain experience in an industrial environment and creates a network of contacts.
LernzielThe main objective of the iinternship is to expose students to the industrial work environment. During this period, students have the opportunity to be involved in on-going projects at the host institution.
InhaltInternship in a computer science company, which is admitted by the CS Department at ETH. Minimum 10 weeks fulltime employment.
Voraussetzungen / BesonderesUm das Industriepraktikum anerkennen zu lassen, müssen bis spätestens zwei Wochen nach Beginn des Praktikums folgende Informationen auf dem Studiensekretariat abgeliefert werden:
- Eine deatillierte Aufgabenbeschreibung
- Die Dauer des Praktikums
- Name des Betreuers sowie akademischer Grad
Freie Wahlfächer (nur für Regl. 2009)
Den Studierenden steht das gesamte Lehrangebot auf Master Level der ETH Zürich, der EPF Lausanne, der Universität Zürich und - nach vorgängiger Genehmigung durch den Studiendirektor - der übrigen Schweizer Universitäten zur individuellen Auswahl offen.

Weitere Details gemäss Art. 31 des Studienreglementes 2009 für den Master-Studiengang Informatik.
NummerTitelTypECTSUmfangDozierende
263-0610-00LDirect Doctorate Research Project
Only for Direct Doctorate Students
O15 KP23AProfessor/innen
KurzbeschreibungDirect Doctorate Students join a research group of D-INFK in order to acquire a broader view of the different research groups and areas.
LernzielStudents extend their knowledge of the different research topics and improve their scientific approach of working on an actual research project.
Inhalt2nd semester students join a research group of D-INFK in order to acquire a broader view of the different research groups and areas. The research group chosen must not be identical with the one, in which the thesis project is conducted.
Voraussetzungen / BesonderesPlease be aware that the research project and the master's thesis have to be coached by two different research groups!
263-0620-00LDirect Doctorate Research Plan
Only for Direct Doctorate Students
O15 KP23AProfessor/innen
KurzbeschreibungThe research plan aims at planning and structuring a student's research work and thesis. It further contributes to the student's ability to write research proposals.
LernzielThe student has to present the research plan to the faculty members in order to defend his/her research goals, but also to demonstrate a solid knowledge on the background literature as well as the planned and alternative procedures to follow.
GESS Wissenschaft im Kontext
Nicht mehr als sechs Kreditpunkte werden in dieser Kategorie akzeptiert.
» siehe Studiengang GESS Wissenschaft im Kontext: Sprachkurse ETH/UZH
» siehe Studiengang GESS Wissenschaft im Kontext: Typ A: Förderung allgemeiner Reflexionsfähigkeiten
» Empfehlungen aus dem Bereich GESS Wissenschaft im Kontext (Typ B) für das D-INFK.
Master-Arbeit
NummerTitelTypECTSUmfangDozierende
263-0800-00LMaster's Thesis Information Belegung eingeschränkt - Details anzeigen
Zur Master-Arbeit wird nur zugelassen, wer:
a. das Bachelor-Studium erfolgreich abgeschlossen hat;
b. allfällige Auflagen für die Zulassung zum Master-Studiengang erfüllt hat;
c. in der Kategorie "Vertiefungsübergreifende Fächer" sind 12 KP;
d. und in der Kategorie "Vertiefungsfächer" sind 26 KP erarbeitet.
O30 KP64DBetreuer/innen
KurzbeschreibungThe Master's thesis concludes the study programme. Thesis work should prove the students' ability to independent, structured and scientific working.
LernzielTo work independently and to produce a scientifically structured work under the supervision of a Computer Science Professor.
InhaltIndependent project work supervised by a Computer Science professor. Duration 6 months.
Voraussetzungen / BesonderesSupervisor must be a professor at D-INFK or affiliated,
see https://inf.ethz.ch/people/faculty.html
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