Gunnar Rätsch: Catalogue data in Autumn Semester 2018
|Name||Prof. Dr. Gunnar Rätsch|
Professur für Biomedizininformatik
ETH Zürich, CAB F 53.2
|Telephone||+41 44 632 20 36|
|252-0945-07L||Doctoral Seminar Machine Learning (HS18) |
Only for Computer Science Ph.D. students.
This doctoral seminar is intended for PhD students affiliated with the Instutute 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 credits||2S||J. M. Buhmann, 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.|
|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.
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 credits||2S||J. M. Buhmann, 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.|
|261-5100-00L||Computational Biomedicine |
Number of participants limited to 60.
|4 credits||2V + 1U||G. Rätsch|
|Abstract||The course critically reviews central problems in Biomedicine and discusses the technical foundations and solutions for these problems.|
|Objective||Over 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.|
|Content||The 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.
|Prerequisites / Notice||Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line|
|551-1299-00L||Introduction to Bioinformatics |
Number of participants limited to 50.
|6 credits||4G||S. Sunagawa, M. Gstaiger, A. Kahles, G. Rätsch, B. Snijder, E. Vayena, C. von Mering, N. Zamboni|
|Abstract||This course introduces principle concepts, the state-of-the-art and methods used in the field of Bioinformatics. Major 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.|
|Objective||The course will provide students with the 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 thus be able to access and analyze 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.|
Case studies to learn about applying ethical principles in human genomics research
Genetic variant calling
Analyze and critical evaluate genome wide association studies
Reconstruction of microbial genomes
Microbial community compositional analysis
Inference of molecular networks
Use of networks for interpretation of (gen)omics data
High throughput single cell imaging
Automatic analysis of drug effects on single cell suspension (chemotyping)
|Prerequisites / Notice||Bringing your own laptop is a prerequisite for taking this course.|