Thomas Hofmann: Catalogue data in Autumn Semester 2019
|Name||Prof. Dr. Thomas Hofmann|
ETH Zürich, CAB F 48.1
|252-0945-09L||Doctoral Seminar Machine Learning (HS19) |
Only for Computer Science Ph.D. students.
This doctoral seminar is intended for PhD students affiliated with the Institute for Machine Learning. Other PhD students who work on machine learning projects or related topics need approval by at least one of the organizers to register for the seminar.
|2 credits||2S||J. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch|
|Abstract||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.|
|Objective||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.|
|Prerequisites / Notice||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.|
|252-4811-00L||Machine Learning Seminar |
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.
|2 credits||2S||T. Hofmann, G. Rätsch|
|Abstract||Seminal and recent papers in machine learning are presented and discussed.|
|Objective||The seminar familiarizes students with advanced and recent ideas in machine learning. Original articles have to be presented, contexctualized, and critically reviewed. The students will learn how to structure a scientific presentation in English which covers the key ideas of a scientific paper.|
|Content||The seminar will cover a number of recent papers which have emerged as important contributions in the machine learning research community. 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.|
|Literature||The papers will be presented and allocated in the first session of the seminar.|
|Prerequisites / Notice||Basic knowledge of machine learning as taught in undergraduate courses such as "252-0220-00 Introduction to Machine Learning" are required.|
|263-3210-00L||Deep Learning||5 credits||2V + 1U + 1A||T. Hofmann|
|Abstract||Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.|
|Objective||In recent years, deep learning and deep networks have significantly improved the state-of-the-art in many application domains such as computer vision, speech recognition, and natural language processing. This class will cover the mathematical foundations of deep learning and provide insights into model design, training, and validation. The main objective is a profound understanding of why these methods work and how. There will also be a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.|
|Prerequisites / Notice||This is an advanced level course that requires some basic background in machine learning. More importantly, students are expected to have a very solid mathematical foundation, including linear algebra, multivariate calculus, and probability. The course will make heavy use of mathematics and is not (!) meant to be an extended tutorial of how to train deep networks with tools like Torch or Tensorflow, although that may be a side benefit.|
The participation in the course is subject to the following condition:
- Students must have taken the exam in Advanced Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below:
Advanced Machine Learning
Computational Intelligence Lab
Introduction to Machine Learning
Statistical Learning Theory
Probabilistic Artificial Intelligence
|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|