Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Doktorat Departement Informationstechnologie und Elektrotechnik Information
Mehr Informationen unter: Link
Lehrangebot Doktorat und Postdoktorat
A minimum of 12 ECTS credit points must be obtained during doctoral studies.

The courses on offer below are but a small selection out of a much larger available number of courses. Please discuss your course selection with your PhD supervisor.
NummerTitelTypECTSUmfangDozierende
227-0559-00LSeminar in Deep Reinforcement Learning Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 25.
W2 KP2SR. Wattenhofer, O. Richter
KurzbeschreibungIn 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.
LernzielSince 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.
InhaltTwo introductory courses introducing Q-learning and policy gradient methods. Afterwards participating students present recent papers. For details see: Link
SkriptSlides of presentations will be made available.
LiteraturOpenAI course (Link) plus selected papers.
The paper selection can be found on Link.
Voraussetzungen / BesonderesIt is expected that student have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses.
227-0690-11LAdvanced Topics in Control (Spring 2020)
New topics are introduced every year.
W4 KP2V + 2UG. Banjac
KurzbeschreibungAdvanced Topics in Control (ATIC) covers advanced research topics in control theory. It is offered each Spring semester with the topic rotating from year to year. Repetition for credit is possible, with consent of the instructor.
LernzielDuring Spring 2020 the course will cover a range of topics in large-scale convex optimization. The students should be able to apply various numerical methods to solve large-scale optimization problems arising in control, machine learning, signal processing, and finance.
InhaltConvex analysis and methods for large-scale optimization. Topics will include: convex sets and functions ; duality theory ; optimality and infeasibility conditions ; structured optimization problems ; gradient-based methods ; operator splitting methods ; distributed and decentralized optimization ; applications in various research areas.
SkriptCopies of the projection slides will be made available on the course Moodle platform.
LiteraturThe course will be largely based on the Large-Scale Convex Optimization course taught at Lund University: Link
Voraussetzungen / BesonderesSufficient mathematical maturity, in particular in linear algebra and analysis.
227-0559-10LSeminar in Communication Networks: Learning, Reasoning and Control Belegung eingeschränkt - Details anzeigen
Findet dieses Semester nicht statt.
Number of participants limited to 24.
W2 KP2SL. Vanbever, A. Singla
KurzbeschreibungIn 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.
LernzielThe 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.
InhaltThe 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: Link
SkriptThe slides of each presentation will be made available on the website.
LiteraturThe paper selection will be made available on the course website: Link
Voraussetzungen / BesonderesCommunication 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.
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