Fan Yang: Catalogue data in Autumn Semester 2020 |
| Name | Prof. Dr. Fan Yang |
| Field | Computer Science |
| Address | Professur für Informatik ETH Zürich, CAB G 19.1 Universitätstrasse 6 8092 Zürich SWITZERLAND |
| fan.yang@inf.ethz.ch | |
| Department | Computer Science |
| Relationship | Assistant Professor (Tenure Track) |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 252-5051-00L | Advanced Topics in Machine Learning   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. | 2 credits | 2S | J. M. Buhmann, G. Rätsch, J. Vogt, F. Yang | |
| Abstract | 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. | ||||
| Learning objective | The 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. | ||||
| Content | 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. | ||||
| Literature | The papers will be presented in the first session of the seminar. | ||||
| 263-3300-00L | Data Science Lab Only for Data Science MSc. | 14 credits | 9P | C. Zhang, V. Boeva, R. Cotterell, J. Vogt, F. Yang | |
| Abstract | In this class, we bring together data science applications provided by ETH researchers outside computer science and teams of computer science master's students. Two to three students will form a team working on data science/machine learning-related research topics provided by scientists in a diverse range of domains such as astronomy, biology, social sciences etc. | ||||
| Learning objective | The goal of this class if for students to gain experience of dealing with data science and machine learning applications "in the wild". Students are expected to go through the full process starting from data cleaning, modeling, execution, debugging, error analysis, and quality/performance refinement. | ||||
| Prerequisites / Notice | Prerequisites: At least 8 KP must have been obtained under Data Analysis and at least 8 KP must have been obtained under Data Management and Processing. | ||||
| 401-5680-00L | Foundations of Data Science Seminar | 0 credits | P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, G. Rätsch, C. Uhler, S. van de Geer, F. Yang | ||
| Abstract | Research colloquium | ||||
| Learning objective | |||||