Thomas Hofmann: Catalogue data in Autumn Semester 2014

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
252-0341-01LInformation Retrieval Information 4 credits2V + 1UT. Hofmann
AbstractIntroduction 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.
ObjectiveIn 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.
252-5051-00LAdvanced Topics in Machine Learning Information Restricted registration - show details 2 credits2SJ. M. Buhmann, T. Hofmann, A. Krause
AbstractIn 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.
ObjectiveThe seminar "Advanced Topics in Pattern Recognition" 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.
ContentThe 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.
LiteratureThe papers will be presented in the first session of the seminar.
263-3010-00LBig Data Information 6 credits3V + 1U + 1AT. Hofmann
AbstractOne of the key challenges of the information society is to turn data into information, information into knowledge, and knowledge into value. To turn data into value in this way involves collecting large volumes of data, possibly from many and diverse data sources, processing the data fast, and applying complex operations to the data.
ObjectiveOne of the key challenges of the information society is to turn data into information, information into knowledge, and knowledge into value. To turn data into value in this way involves collecting large volumes of data, possibly from many and diverse data sources, processing the data fast, and applying complex operations to the data. This combination of requirements is typically referred to as Big Data and it has led to a completely new way to do business (e.g., develop new products and business models) and do science (sometimes referred to as data-driven science or the "fourth paradigm"). Unfortunately, big data grows faster than our ability to process the data so that new architectures and approaches for processing Big Data are needed.
ContentThe goal of this course is to give an overview of Big Data technologies. All aspects are covered: data formats and models, programming languages, optimization techniques, systems, and applications.
LiteraturePapers from scientific conferences and journals. References will be given as part of the course material during the semester.