Suchergebnis: Katalogdaten im Frühjahrssemester 2020

Nummer	Titel	Typ	ECTS	Umfang	Dozierende
Informatik Master
Vertiefungsfächer
Vertiefung in Visual Computing
Seminar in Visual Computing
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.
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.
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.

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