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Machine Learning for Health Care
Last Updated: 2026-02-05 16:08:04
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)
- Main link
- Information
General Information
- Language
- English
- Levels
- 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.
Registration & Places
- Max Places
- 150
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Machine Learning for Health Care |
|
2 h weekly |
| independent project | Machine Learning for Health Care |
|
2 h weekly |
Offered In
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Recommended Elective Courses (These courses are particularly recommended for the Bioelectronics track. Please consult your track adviser if you wish to select other subjects.)
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Computational Biology and Bioinformatics Master (More informations at: )
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Advanced Courses (A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof at least 16 ECTS in the Theory and 10 ECTS in the Biology category. Note that some of the lectures are being recorded: )
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Theory (At least 16 ECTS need to be acquired in this category.)
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Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)
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