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261-5120-00L 5 Credits BSC , MSC , WBZ D-BSSE , D-MAVT , D-INFK , D-MATH , D-PHYS , D-ITET , D-HEST

Machine Learning for Health Care

Lecturers & Examiners: Prof. Dr. Gunnar Rätsch, Prof. Dr. Valentina Boeva, Dr. Irene Cannistraci
VVZ CR 3.4

Last Updated: 2026-06-03 00:14:06

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 different topic clusters that will cover the most relevant applications of ML in Health Care, as e.g. 1) Structured time series: Temporal time series of structured data often appear in biomedical datasets, presenting challenges such as containing variables with different periodicities or being influenced by static data. 2) Medical notes: Many medical observations are stored as free text. We will analyze strategies for extracting knowledge from these textual records. 3) Medical images: Images are a fundamental piece of information in many medical disciplines. We will study how to train ML algorithms to analyze and interpret medical images effectively. 4) Genomics data: ML in genomics is still an emerging subfield. However, given that genomics data are arguably the most extensive and complex datasets in biomedicine, we expect many relevant ML applications to arise in the near future. We will review and discuss current applications and challenges in genomics data analysis. 5) Explainable/Interpretable ML: Interpretable and explainable machine learning focuses on the design of human-understandable models and algorithms that allow for black-box model introspection after training, i.e., post hoc. We will explore methods to make ML models more transparent, particularly in the context of healthcare. 6) Representation Learning: Representation learning is a crucial aspect of ML in healthcare. It involves learning meaningful representations from data, which can be especially valuable in tasks like medical image analysis, feature extraction from biomedical time series data, or dimensionality reduction. We will delve into techniques for efficient representation learning to enhance the performance of healthcare ML models.

Resources

Learning Materials (Links)

General Information

Language
English
Levels
BSC , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 180 minutes
Aids
1 page (single side) of A4 paper is allowed for notes in the exam. The notes may be typed (font restriction: minimal font 10pt) or handwritten.
Digital
The examination takes place on your own device. Installation of SEB required.
The course grading consists of two components: 70% session examination and 30% project work, with the final grade determined by the weighted average of these elements. The project work is a compulsory performance assessment and must be passed independently to successfully complete the course.The project work requires approximately 30 hours of total effort and involves completing two projects and presenting a research paper, either individually or as part of a group. In addition, students are required to regularly attend their peers' research paper presentations and complete multiple-choice questions based on the content presented. Achieving a specified proportion of correct answers on these questions is necessary to qualify for the session examination.Failing the project results in a failing grade for the entire course, Machine Learning for Health Care (261-5120-00L). Students who fail the project or who do not qualify for the examination due to insufficient performance on the multiple-choice questions must de-register from the exam. Otherwise, they will not be admitted and will be marked as no-shows.

Registration & Places

Max Places
150

Course Components

Type Title Time & Place Hours
lecture Machine Learning for Health Care
  • Mon 10:15-12:00 (HG E 5)
2 h weekly
independent project Machine Learning for Health Care
  • Tue 13:15-14:00 (HG D 7.2)
2 h weekly

Offered In