Search result: Catalogue data in Autumn Semester 2021

Computer Science Master Information
Master Studies (Programme Regulations 2020)
Seminar
NumberTitleTypeECTSHoursLecturers
252-3811-00LCase Studies from Practice Seminar Restricted registration - show details
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.
W4 credits2SM. Brandis
AbstractParticipants will learn how to analyze and solve IT problems in practice in a systematic way, present findings to decision bodies, and defend their conclusions.
ObjectiveParticipants understand the different viewpoints for IT-decisions in practice, including technical and business aspects, can effectively analyze IT questions from the different viewpoints and facilitate decision making.
ContentParticipants learn how to systematically approach an IT problem in practice. They work in groups of three to solve a case from a participating company in depth, studying provided materials, searching for additional information, analyzing all in depth, interviewing members from the company or discussing findings with them to obtain further insights, and presenting and defending their conclusion to company representatives, the lecturer, and all other participants of the seminar. Participants also learn how to challenge presentations from other teams, and obtain an overview of learnings from the cases other teams worked on.
Lecture notesMethodologies to analyze the cases and create final presentations. Short overview of each case.
Prerequisites / NoticeSuccessful completion of Lecture "Case Studies from Practice".
252-4601-00LCurrent Topics in Information Security Information Restricted registration - show details
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 credits2SS. Capkun, K. Paterson, A. Perrig, S. Shinde
AbstractThe 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.
ObjectiveThe 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.
ContentThe 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.

Selected Topics

- 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
LiteratureThe reading list will be published on the course web site.
252-5051-00LAdvanced Topics in Machine Learning Information Restricted registration - show details
Number of participants limited to 40.

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.
W2 credits2SJ. M. Buhmann, R. Cotterell, J. Vogt, F. Yang
AbstractIn 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.
ObjectiveThe 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.
ContentThe 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.
LiteratureThe papers will be presented in the first session of the seminar.
252-5701-00LAdvanced Topics in Computer Graphics and Vision Information Restricted registration - show details
Number of participants limited to 24.

The deadline for deregistering expires at the end of the third week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
W2 credits2SM. Pollefeys, O. Sorkine Hornung, S. Tang
AbstractThis 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.
ObjectiveThe 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.
ContentThis 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.
Lecture notesno script
LiteratureIndividual research papers are selected each term. See Link for the current list.
263-2100-00LResearch Topics in Software Engineering Information Restricted registration - show details
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 credits2SP. Müller, M. Püschel
AbstractThis seminar is an opportunity to become familiar with current research in software engineering and more generally with the methods and challenges of scientific research.
ObjectiveEach 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).
ContentThe 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.
LiteratureThe publications to be presented will be announced on the seminar home page at least one week before the first session.
Prerequisites / NoticeOrganizational note: the seminar will meet only when there is a scheduled presentation. Please consult the seminar's home page for information.
263-3504-00LHardware Acceleration for Data Processing Information Restricted registration - show details
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 credits2SG. Alonso
AbstractThe seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.
ObjectiveThe seminar will cover topics related to data processing using new hardware in general and hardware accelerators (GPU, FPGA, specialized processors) in particular.
ContentThe 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.
Prerequisites / NoticeStudents taking this seminar should have the necessary background in systems and low level programming.
263-3713-00LAdvanced Topics in Human-Centric Computer Vision Information Restricted registration - show details
Numbers of participants limited to 20.

The deadline for deregistering expires at the end of the third week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
W2 credits2SO. Hilliges
AbstractIn this seminar we will discuss state-of-the-art literature on human-centric computer vision topics including but not limited to human pose estimation, hand and eye-gaze estimation as well as generative modeliing of detailed human activities.
ObjectiveThe learning objective is to analyze selected research papers published at top computer vision and machine learning venues. A key focus will be placed on identifying and discussing open problems and novel solutions in this space. The seminar will achieve this via several components: reading papers, technical presentations, writing analysis and critique summaries, class discussions, and exploration of potential research topics.
ContentThe goal of the seminar is not only to familiarize students with exciting new research topics, but also to teach basic scientific writing and oral presentation skills. The seminar will have a different structure from regular seminars to encourage more discussion and a deeper learning experience.

We will treat papers as case studies and discuss them in-depth in the seminar. Once per semester, every student will have to take one of the following roles:

Presenter: Give a presentation about the paper that you read in depth.

Reviewer: Perform a critical review of the paper.

All other students: read the paper and submit questions they have about the paper before the presentation.
Prerequisites / NoticeParticipation will be limited subject to available topics. Furthermore, students will have to submit a motivation paragraph. Participants will be selected based on this paragraph.
CompetenciesCompetencies
Method-specific CompetenciesAnalytical Competenciesassessed
Social CompetenciesCommunicationassessed
Personal CompetenciesCritical Thinkingassessed
263-4410-00LSeminar on Advanced Graph Algorithms and Optimization Information Restricted registration - show details
Number of participants limited to 6!
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 credits2SR. Kyng
AbstractThis seminar aims to familiarize students with current research topics in fast graph algorithms and optimization.
ObjectiveRead papers on cutting edge research topics; learn how to give a scientific talk.
ContentWe will study recent papers that made significant contributions in the areas in fast graph algorithms and optimization.
Prerequisites / NoticeAs prerequisite we require that you passed the course "Advanced Graph Algorithms and Optimization". In exceptional cases, students who passed one of the courses "Randomized Algorithms and Probabilistic Methods", "Optimization for Data Science", or "Advanced Algorithms" may also participate, at the discretion of the lecturer.
263-5156-00LBeyond iid Learning: Causality, Dynamics, and Interactions Information Restricted registration - show details
Number of participants limited to 60.

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 credits2SM. Mühlebach, A. Krause, B. Schölkopf
AbstractMany machine learning problems go beyond supervised learning on independent data points and require an understanding of the underlying causal mechanisms, the interactions between the learning algorithms and their environment, and adaptation to temporal changes. The course highlights some of these challenges and relates them to state-of-the-art research.
ObjectiveThe goal of this seminar is to gain experience with machine learning research and foster interdisciplinary thinking.
ContentThe seminar will be divided into two parts. The first part summarizes the basics of statistical learning theory, game theory, causal inference, and dynamical systems in four lectures. This sets the stage for the second part, where distinguished speakers will present selected aspects in greater detail and link them to their current research.

Keywords: Causal inference, adaptive decision-making, reinforcement learning, game theory, meta learning, interactions with humans.
Lecture notesFurther information will be published on the course website: Link
Prerequisites / NoticeBSc in computer science or related field (engineering, physics, mathematics). Passed at least one learning course, such as ``Introduction to Machine Learning" or ``Probabilistic Artificial Intelligence".
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