VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.

261-5120-00L 5 Credits MSC , WBZ D-BSSE , D-INFK , D-MATH , D-ITET
You're viewing possible stale or outdated data. Please check the latest semester for more up-to-date information.

Machine Learning for Health Care

Lecturers & Examiners: Prof. Dr. Gunnar Rätsch, Prof. Dr. Julia Vogt, Prof. Dr. Valentina Boeva
Number of participants limited to 150.
VVZ CR 3.4

Last Updated: 2026-02-05 15:55:03

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.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 180 minutes
Aids
1 sheet A4 (= 2 pages) handwritten summary
70% session examination, 30% project/presentation; the final grade will be calculated as weighted average of both these elements. As a compulsory continuous performance assessment task, the project/presentation must be passed on its own and has a bonus/penalty function.The projects/presentations are an integral part (30 hours of work, 1 credits) of the course and consists of a practical part and/or a presentation of a research paper. Participation is mandatory. Failing the project results in a failing grade for the overall examination of Machine Learning for Health Care (261-5120-00L).Students who fail to fulfill the project/presentation requirement have to de-register from the exam. Otherwise, they are not admitted to the exam and they will be treated as a no show.

Registration & Places

Max Places
150

Course Components

Type Title Time & Place Hours
practical/laboratory course Machine Learning for Health Care
  • Tue 10:15-12:00 (HG D 7.2)
  • Tue 13:15-14:00 (HG D 7.2)
3 h weekly
independent project Machine Learning for Health Care No time listed 1 h weekly

Offered In