From 2 November 2020, the autumn semester 2020 will take place online. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers via e-mail.

261-5120-00L  Machine Learning for Health Care

SemesterSpring Semester 2019
LecturersG. Rätsch
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 78.

Previously called Computational Biomedicine II

Catalogue data

AbstractThe 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.
ObjectiveDuring 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.
ContentThe 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 / NoticeData 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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersG. Rätsch
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 180 minutes
Additional information on mode of examination70% 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.
Written aids2 sheets A4 (= 4 pages) summary, script
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

Main linkInformation
Only public learning materials are listed.


261-5120-00 PMachine Learning for Health Care3 hrs
Thu15-18LFW C 5 »
G. Rätsch


No information on groups available.


Places60 at the most
Waiting listuntil 03.03.2019

Offered in

CAS in Computer ScienceFocus Courses and ElectivesWInformation
Data Science MasterInterdisciplinary ElectivesWInformation
Computer Science MasterElective Focus Courses General StudiesWInformation
Computer Science MasterFocus Elective Courses Computational ScienceWInformation