Gunnar Rätsch: Catalogue data in Spring Semester 2022 |
| Name | Prof. Dr. Gunnar Rätsch |
| Field | Biomedical |
| Address | Professur für Biomedizininformatik ETH Zürich, CAB F 53.2 Universitätstrasse 6 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 20 36 |
| raetsch@inf.ethz.ch | |
| URL | http://bmi.inf.ethz.ch |
| Department | Computer Science |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 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. | ||||
| Learning 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 | ||||
| 252-0945-14L | Doctoral Seminar Machine Learning (FS22) 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, 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. | ||||
| Learning 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. | ||||
| 261-5120-00L | Machine Learning for Health Care Number of participants limited to 150. | 5 credits | 2V + 2A | 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. | ||||
| Learning 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. | ||||
| 263-0008-00L | Computational Intelligence Lab Only for master students, otherwise a special permission by the study administration of D-INFK is required. | 8 credits | 2V + 2U + 3A | G. Rätsch | |
| Abstract | This laboratory course teaches fundamental concepts in computational science and machine learning with a special emphasis on matrix factorization and representation learning. The class covers techniques like dimension reduction, data clustering, sparse coding, and deep learning as well as a wide spectrum of related use cases and applications. | ||||
| Learning objective | Students acquire fundamental theoretical concepts and methodologies from machine learning and how to apply these techniques to build intelligent systems that solve real-world problems. They learn to successfully develop solutions to application problems by following the key steps of modeling, algorithm design, implementation and experimental validation. This lab course has a strong focus on practical assignments. Students work in groups of three to four people, to develop solutions to three application problems: 1. Collaborative filtering and recommender systems, 2. Text sentiment classification, and 3. Road segmentation in aerial imagery. For each of these problems, students submit their solutions to an online evaluation and ranking system, and get feedback in terms of numerical accuracy and computational speed. In the final part of the course, students combine and extend one of their previous promising solutions, and write up their findings in an extended abstract in the style of a conference paper. (Disclaimer: The offered projects may be subject to change from year to year.) | ||||
| Content | see course description | ||||