Name | Herr Prof. Dr. Andreas Krause |
Lehrgebiet | Informatik |
Adresse | Institut für Maschinelles Lernen ETH Zürich, OAT Y 13.1 Andreasstrasse 5 8092 Zürich SWITZERLAND |
Telefon | +41 44 632 63 22 |
Fax | +41 44 623 15 62 |
krausea@ethz.ch | |
URL | http://las.ethz.ch/krausea.html |
Departement | Informatik |
Beziehung | Ordentlicher Professor |
Nummer | Titel | ECTS | Umfang | Dozierende | |
---|---|---|---|---|---|
252-0945-00L | Doctoral Seminar Machine Learning | 2 KP | 2S | J. M. Buhmann, A. Krause | |
Kurzbeschreibung | An essential aspect of any research project is dissemination of the findings arising from the study. Here we focus on oral communication, which includes: appropriate selection of material, preparation of the visual aids (slides and/or posters), and presentation skills. | ||||
Lernziel | The seminar participants should learn how to prepare and deliver scientific talks as well as to deal with technical questions. Participants are also expected to actively contribute to discussions during presentations by others, thus learning and practicing critical thinking skills. | ||||
Voraussetzungen / Besonderes | This doctoral seminar of the Machine Learning Laboratory of ETH is intended for PhD students who work on a machine learning project, i.e., for the PhD students of the ML lab. | ||||
252-5051-00L | Advanced Topics in Machine Learning | 2 KP | 2S | J. M. Buhmann, T. Hofmann, A. Krause | |
Kurzbeschreibung | In 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. | ||||
Lernziel | The seminar "Advanced Topics in Pattern Recognition" 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. | ||||
Inhalt | The 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. | ||||
Literatur | The papers will be presented in the first session of the seminar. | ||||
263-5210-00L | Probabilistic Artificial Intelligence | 4 KP | 2V + 1U | A. Krause | |
Kurzbeschreibung | This course introduces core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. | ||||
Lernziel | How can we build systems that perform well in uncertain environments and unforeseen situations? How can we develop systems that exhibit "intelligent" behavior, without prescribing explicit rules? How can we build systems that learn from experience in order to improve their performance? We will study core modeling techniques and algorithms from statistics, optimization, planning, and control and study applications in areas such as sensor networks, robotics, and the Internet. The course is designed for upper-level undergraduate and graduate students. | ||||
Inhalt | Topics covered: - Search (BFS, DFS, A*), constraint satisfaction and optimization - Tutorial in logic (propositional, first-order) - Probability - Bayesian Networks (models, exact and approximative inference, learning) - Temporal models (Hidden Markov Models, Dynamic Bayesian Networks) - Probabilistic palnning (MDPs, POMPDPs) - Reinforcement learning - Combining logic and probability | ||||
Voraussetzungen / Besonderes | Solid basic knowledge in statistics, algorithms and programming |