Machine Learning (ML) methods have shown to have a profound impact in medical 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 medicine, and work on practical projects to solve medical problems with the help of ML.
Lernziel
The course will start with a general introduction to ML, where we will cover supervised and unsupervised learning techniques, as for example classification and regression models, feature selection and preprocessing of data, clustering and dimensionality reduction techniques. After the introduction of the basic methodologies, we will continue with the most relevant applications of ML in medicine, as for example dealing with time series, medical notes and medical images.
Inhalt
During the last few years, we have observed a rapid growth of Machine Learning (ML) in Medicine. ML methods have shown to have a profound impact in medical 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 medicine, discuss the main challenges they present and their current technical solutions, and work on practical projects to solve medical problems with the help of ML.
Leistungskontrolle
Information zur Leistungskontrolle (gültig bis die Lerneinheit neu gelesen wird)
Repetition nur nach erneuter Belegung der Lerneinheit möglich.
Zusatzinformation zum Prüfungsmodus
Absences must be approved in advance by the Director of Studies. The written request must be submitted to the principal lecturer and the Director of Studies at least 1 week in advance. The request will be examined with reservations.
Lernmaterialien
Keine öffentlichen Lernmaterialien verfügbar.
Es werden nur die öffentlichen Lernmaterialien aufgeführt.
Gruppen
Keine Informationen zu Gruppen vorhanden.
Einschränkungen
Vorrang
Die Belegung der Lerneinheit ist nur durch die primäre Zielgruppe möglich
Leistungskontrolle als Semesterkurs