261-5120-00L  Machine Learning for Health Care

SemesterFrühjahrssemester 2021
DozierendeV. Boeva, G. Rätsch, J. Vogt
Periodizitätjährlich wiederkehrende Veranstaltung
LehrspracheEnglisch
KommentarNumber of participants limited to 150.



Lehrveranstaltungen

NummerTitelUmfangDozierende
261-5120-00 PMachine Learning for Health Care3 Std.
Di10-12HG D 7.2 »
13-14HG D 7.2 »
V. Boeva, G. Rätsch, J. Vogt
261-5120-00 AMachine Learning for Health Care1 Std.V. Boeva, G. Rätsch, J. Vogt

Katalogdaten

KurzbeschreibungThe 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.
LernzielDuring 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.
InhaltThe 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.
Voraussetzungen / BesonderesData 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.

Leistungskontrolle

Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Leistungskontrolle als Semesterkurs
ECTS Kreditpunkte5 KP
PrüfendeG. Rätsch, V. Boeva, J. Vogt
FormSessionsprüfung
PrüfungsspracheEnglisch
RepetitionDie Leistungskontrolle wird in jeder Session angeboten. Die Repetition ist ohne erneute Belegung der Lerneinheit möglich.
Prüfungsmodusschriftlich 180 Minuten
Zusatzinformation zum Prüfungsmodus70% 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.
Hilfsmittel schriftlich1 sheet A4 (= 2 pages) handwritten summary
Diese Angaben können noch zu Semesterbeginn aktualisiert werden; verbindlich sind die Angaben auf dem Prüfungsplan.

Lernmaterialien

 
HauptlinkInformation
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Gruppen

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Einschränkungen

PlätzeMaximal 150
WartelisteBis 07.03.2021

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