Thomas Hofmann: Catalogue data in Autumn Semester 2016
|Name||Prof. Dr. Thomas Hofmann|
ETH Zürich, CAB F 48.1
|252-0341-01L||Information Retrieval||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.|
|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.|
|252-0945-03L||Doctoral Seminar Machine Learning (HS16) |
Only for Computer Science Ph.D. students.
|2 credits||2S||J. M. Buhmann, T. Hofmann, A. Krause|
|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.|
|252-5051-00L||Advanced Topics in Machine Learning||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.|
|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 120.
|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.|
|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 fundamentals of deep learning and provide a rich set of hands-on tasks and practical projects to familiarize students with this emerging technology.|
|Prerequisites / Notice||The participation in the course is subject to the following conditions:|
1) The number of participants is limited to 120 students (MSc and PhDs).
2) Students must have taken the exam in Machine Learning (252-0535-00) or have acquired equivalent knowledge.