Gunnar Rätsch: Catalogue data in Spring Semester 2021
|Name||Prof. Dr. Gunnar Rätsch|
Professur für Biomedizininformatik
ETH Zürich, CAB F 53.2
|Telephone||+41 44 632 20 36|
|252-0868-00L||Data Science for Medicine |
Only for Human Medicine BSc
|4 credits||4V||J. Vogt, V. Boeva, G. Rätsch|
|Abstract||Machine Learning (ML) methods have shown to have a profound impact in medical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in medicine, and work on practical projects to solve medical problems with the help of ML.|
|Objective||The course will start with a general introduction to ML, where we will cover supervised and unsupervised learning techniques, as for example classification and regression models, feature selection and preprocessing of data, clustering and dimensionality reduction techniques. After the introduction of the basic methodologies, we will continue with the most relevant applications of ML in medicine, as for example dealing with time series, medical notes and medical images.|
|Content||During the last few years, we have observed a rapid growth of Machine Learning (ML) in Medicine. ML methods have shown to have a profound impact in medical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in medicine, discuss the main challenges they present and their current technical solutions, and work on practical projects to solve medical problems with the help of ML.|
|Prerequisites / Notice||Prerequisite:|
Attendance/exam of 252-0866-00 Digital Medicine I
|252-0945-12L||Doctoral Seminar Machine Learning (FS21)|
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 credits||1S||N. He, M. Sachan, 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.|
|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.|
|261-5113-00L||Computational Challenges in Medical Genomics |
Number of participants limited to 20.
|2 credits||2S||A. Kahles, G. Rätsch|
|Abstract||This seminar discusses recent relevant contributions to the fields of computational genomics, algorithmic bioinformatics, statistical genetics and related areas. Each participant will hold a presentation and lead the subsequent discussion.|
|Objective||Preparing and holding a scientific presentation in front of peers is a central part of working in the scientific domain. In this seminar, the participants will learn how to efficiently summarize the relevant parts of a scientific publication, critically reflect its contents, and summarize it for presentation to an audience. The necessary skills to succesfully present the key points of existing research work are the same as needed to communicate own research ideas. |
In addition to holding a presentation, each student will both contribute to as well as lead a discussion section on the topics presented in the class.
|Content||The topics covered in the seminar are related to recent computational challenges that arise from the fields of genomics and biomedicine, including but not limited to genomic variant interpretation, genomic sequence analysis, compressive genomics tasks, single-cell approaches, privacy considerations, statistical frameworks, etc.|
Both recently published works contributing novel ideas to the areas mentioned above as well as seminal contributions from the past are amongst the list of selected papers.
|Prerequisites / Notice||Knowledge of algorithms and data structures and interest in applications in genomics and computational biomedicine.|
|261-5120-00L||Machine Learning for Health Care |
Number of participants limited to 150.
|5 credits||3P + 1A||V. Boeva, G. Rätsch, J. Vogt|
|Abstract||The course will review the most relevant methods and applications of Machine Learning in Biomedicine, discuss the main challenges they present and their current technical problems.|
|Objective||During the last years, we have observed a rapid growth in the field of Machine Learning (ML), mainly due to improvements in ML algorithms, the increase of data availability and a reduction in computing costs. This growth is having a profound impact in biomedical applications, where the great variety of tasks and data types enables us to get benefit of ML algorithms in many different ways. In this course we will review the most relevant methods and applications of ML in biomedicine, discuss the main challenges they present and their current technical solutions.|
|Content||The course will consist of four topic clusters that will cover the most relevant applications of ML in Biomedicine: |
1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges as containing variables with different periodicities, being conditioned by static data, etc.
2) Medical notes: Vast amount of medical observations are stored in the form of free text, we will analyze stategies for extracting knowledge from them.
3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms with them.
4) Genomics data: ML in genomics is still an emerging subfield, but given that genomics data are arguably the most extensive and complex datasets that can be found in biomedicine, it is expected that many relevant ML applications will arise in the near future. We will review and discuss current applications and challenges.
|Prerequisites / Notice||Data Structures & Algorithms, Introduction to Machine Learning, Statistics/Probability, Programming in Python, Unix Command Line|
Relation to Course 261-5100-00 Computational Biomedicine: This course is a continuation of the previous course with new topics related to medical data and machine learning. The format of Computational Biomedicine II will also be different. It is helpful but not essential to attend Computational Biomedicine before attending Computational Biomedicine II.
|401-5680-00L||Foundations of Data Science Seminar||0 credits||P. L. Bühlmann, A. Bandeira, H. Bölcskei, J. M. Buhmann, T. Hofmann, A. Krause, A. Lapidoth, H.‑A. Loeliger, M. H. Maathuis, N. Meinshausen, G. Rätsch, S. van de Geer, F. Yang|