Thomas Hofmann: Catalogue data in Autumn Semester 2017

Name	Prof. Dr. Thomas Hofmann
Field	Data Analytics
Address	Dep. Informatik ETH Zürich, CAB F 48.1 Universitätstrasse 6 8092 Zürich SWITZERLAND
E-mail	thomas.hofmann@inf.ethz.ch
URL	http://www.inf.ethz.ch/department/faculty-profs/person-detail.html?persid=148752
Department	Computer Science
Relationship	Full Professor

Number	Title	ECTS	Hours	Lecturers
252-0341-01L	Information Retrieval Does not take place this semester.	4 credits	2V + 1U	T. Hofmann
Abstract	Introduction to information retrieval with a focus on text documents and images. Main topics comprise extraction of characteristic features from documents, index structures, retrieval models, search algorithms, benchmarking, and feedback mechanisms. Searching the web, images and XML collections demonstrate recent applications of information retrieval and their implementation.
Learning objective	In depth understanding of managing, indexing, and retrieving documents with text, image and XML content. Knowledge about basic search algorithms on the web, benchmarking of search algorithms, and relevance feedback methods.
252-0945-05L	Doctoral Seminar Machine Learning (HS17) Only for Computer Science Ph.D. students.	1 credit	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.
Learning 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.
252-5051-00L	Advanced Topics in Machine Learning Number of participants limited to 40.	2 credits	2S	J. M. Buhmann, T. Hofmann, A. Krause, G. Rätsch
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.
263-3210-00L	Deep Learning Number of participants limited to 300.	4 credits	2V + 1U	T. Hofmann
Abstract	Deep learning is an area within machine learning that deals with algorithms and models that automatically induce multi-level data representations.
Learning 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 conditions: 1) The number of participants is limited to 300 students (MSc and PhDs). 2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge, see exhaustive list below: Machine Learning https://ml2.inf.ethz.ch/courses/ml/ Computational Intelligence Lab http://da.inf.ethz.ch/teaching/2017/CIL/ Learning and Intelligent Systems https://las.inf.ethz.ch/teaching/lis-s17 Statistical Learning Theory http://ml2.inf.ethz.ch/courses/slt/ Computational Statistics https://stat.ethz.ch/education/semesters/ss2012/CompStat/sk.pdf Probabilistic Artificial Intelligence https://las.inf.ethz.ch/teaching/pai-f16 Data Mining: Learning from Large Data Sets https://las.inf.ethz.ch/teaching/dm-f16
851-0147-03L	Relevance and Information Particularly suitable for students of D-INFK	3 credits	2S	M. Hampe, T. Hofmann
Abstract	In the seminar we will compare theories of meaning and information by looking at exemplary texts in this field, e.g. from Paul Grice and Fred Dretske.
Learning objective	Students should become acquainted with the different philosophical explications of what it is for language to have a "content", and especially be able to make up their mind about the difference between intentional and non-intentional conceptions in this field.