Gunnar Rätsch: Katalogdaten im Herbstsemester 2021

NameHerr Prof. Dr. Gunnar Rätsch
LehrgebietBiomedizininformatik
Adresse
Professur für Biomedizininformatik
ETH Zürich, CAB F 53.2
Universitätstrasse 6
8092 Zürich
SWITZERLAND
Telefon+41 44 632 20 36
E-Mailraetsch@inf.ethz.ch
URLhttp://bmi.inf.ethz.ch
DepartementInformatik
BeziehungOrdentlicher Professor

NummerTitelECTSUmfangDozierende
252-0945-13LDoctoral Seminar Machine Learning (HS21)
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 KP1SJ. M. Buhmann, N. He, A. Krause, G. Rätsch, M. Sachan
KurzbeschreibungAn 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.
LernzielThe 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.
Voraussetzungen / BesonderesThis 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 Belegung eingeschränkt - Details anzeigen
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 KP2SV. Boeva, G. Rätsch
KurzbeschreibungSeminal and recent papers in machine learning are presented and discussed.
LernzielThe 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.
InhaltThe 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.
LiteraturThe papers will be presented and allocated in the first session of the seminar.
Voraussetzungen / BesonderesBasic knowledge of machine learning as taught in undergraduate courses such as "252-0220-00 Introduction to Machine Learning" are required.
261-5100-00LComputational Biomedicine Information Belegung eingeschränkt - Details anzeigen
Number of participants limited to 120.
5 KP2V + 1U + 1AV. Boeva, G. Rätsch
KurzbeschreibungThe course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.
LernzielOver the past years, rapid technological advancements have transformed classical disciplines such as biology and medicine into fields of apllied data science. While the sheer amount of the collected data often makes computational approaches inevitable for analysis, it is the domain specific structure and close relation to research and clinic, that call for accurate, robust and efficient algorithms. In this course we will critically review central problems in Biomedicine and will discuss the technical foundations and solutions for these problems.
InhaltThe course will consist of three topic clusters that will cover different aspects of data science problems in Biomedicine:
1) String algorithms for the efficient representation, search, comparison, composition and compression of large sets of strings, mostly originating from DNA or RNA Sequencing. This includes genome assembly, efficient index data structures for strings and graphs, alignment techniques as well as quantitative approaches.
2) Statistical models and algorithms for the assessment and functional analysis of individual genomic variations. this includes the identification of variants, prediction of functional effects, imputation and integration problems as well as the association with clinical phenotypes.
3) Models for organization and representation of large scale biomedical data. This includes ontolgy concepts, biomedical databases, sequence annotation and data compression.
Voraussetzungen / BesonderesData Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line
551-1299-00LIntroduction to Bioinformatics Belegung eingeschränkt - Details anzeigen 6 KP4GS. Sunagawa, M. Gstaiger, A. Kahles, G. Rätsch, B. Snijder, E. Vayena, C. von Mering, N. Zamboni
KurzbeschreibungThis course introduces principle concepts, the state-of-the-art and methods used in some major fields of Bioinformatics. Topics include: genomics, metagenomics, network bioinformatics, and imaging. Lectures are accompanied by practical exercises that involve the use of common bioinformatic methods and basic programming.
LernzielThe course will provide students with theoretical background in the area of genomics, metagenomics, network bioinformatics and imaging. In addition, students will acquire basic skills in applying modern methods that are used in these sub-disciplines of Bioinformatics. Students will be able to access and analyse DNA sequence information, construct and interpret networks that emerge though interactions of e.g. genes/proteins, and extract information based on computer-assisted image data analysis. Students will also be able to assess the ethical implications of access to and generation of new and large amounts of information as they relate to the identifiability of a person and the ownership of data.
InhaltEthics:
Case studies to learn about applying ethical principles in human genomics research

Genomics:
Genetic variant calling
Analysis and critical evaluation of genome wide association studies

Metagenomics:
Reconstruction of microbial genomes
Microbial community compositional analysis
Quantitative metagenomics

Network bioinformatics:
Inference of molecular networks
Use of networks for interpretation of (gen)omics data

Imaging:
High throughput single cell imaging
Image segmentation
Automatic analysis of drug effects on single cell suspension (chemotyping)
Voraussetzungen / BesonderesCourse participants have already acquired basic programming skills in Python and R.

Students will bring and work on their own laptop computers, preferentially running the latest versions of Windows or MacOSX.