Thomas Hofmann: Catalogue data in Autumn Semester 2020

Name Prof. Dr. Thomas Hofmann
FieldData Analytics
Address
Dep. Informatik
ETH Zürich, CAB F 48.1
Universitätstrasse 6
8092 Zürich
SWITZERLAND
E-mailthomas.hofmann@inf.ethz.ch
URLhttp://www.inf.ethz.ch/department/faculty-profs/person-detail.html?persid=148752
DepartmentComputer Science
RelationshipFull Professor

NumberTitleECTSHoursLecturers
252-0945-11LDoctoral Seminar Machine Learning (HS20) Restricted registration - show details
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 credits1SJ. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch
AbstractAn 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.
ObjectiveThe 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 / NoticeThis 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.
252-4811-00LMachine Learning Seminar 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.
2 credits2SV. Boeva, T. Hofmann
AbstractSeminal and recent papers in machine learning are presented and discussed.
ObjectiveThe 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.
ContentThe 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.
LiteratureThe papers will be presented and allocated in the first session of the seminar.
Prerequisites / NoticeBasic knowledge of machine learning as taught in undergraduate courses such as "252-0220-00 Introduction to Machine Learning" are required.
263-3210-00LDeep Learning Information Restricted registration - show details 8 credits3V + 2U + 2AT. Hofmann
AbstractDeep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
ObjectiveIn 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 / NoticeThis 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
https://ml2.inf.ethz.ch/courses/aml/

Computational Intelligence Lab
http://da.inf.ethz.ch/teaching/2019/CIL/

Introduction to Machine Learning
https://las.inf.ethz.ch/teaching/introml-S19

Statistical Learning Theory
http://ml2.inf.ethz.ch/courses/slt/

Computational Statistics
https://stat.ethz.ch/lectures/ss19/comp-stats.php

Probabilistic Artificial Intelligence
https://las.inf.ethz.ch/teaching/pai-f18
266-0100-00LCapstone Project Information Restricted registration - show details
Only for DAS in Data Science.
8 credits17AF. Perez Cruz, O. Verscheure, T. Hofmann
AbstractThe capstone project is part of the DAS in Data Science and is an opportunity to apply the knowledge acquired in the program in an independent, real-world project.
ObjectiveTo apply the knowledge acquired in the program in an independent, real-world project.
ContentThe capstone project can be done under the supervision of the Swiss Data Science Center, or of any core or adjunct faculty of Data Science.
The project has to be finished within 6 months. Deadline for a project the following semester conducted at the SDSC is mid June/mid December.
Prerequisites / NoticeThe minimal prerequisites for the Capstone Project registration are:
- completedFoundation Course
- completed 12 credits in the Specialization Track
401-5680-00LFoundations of Data Science Seminar Information 0 creditsP. 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
AbstractResearch colloquium
Objective