252-0868-00L  Digital Medicine II

SemesterSpring Semester 2020
LecturersJ. Vogt, N. Davidson, G. Rätsch
Periodicityyearly recurring course
Language of instructionEnglish
CommentOnly for Human Medicine BSc



Courses

NumberTitleHoursLecturers
252-0868-00 VDigitale Medizin II4 hrs
23.03.10-17ML F 39 »
24.03. - 03.04.09-14ML F 39 »
13-17CAB H 52 »
13-17CAB H 53 »
13-17CNB G 110 »
13-17HG F 26.1 »
13-17HG F 26.3 »
14-17CAB H 52 »
14-17CAB H 53 »
14-17CNB G 110 »
14-17HG F 26.1 »
14-17HG F 26.3 »
02.04.09-17ML F 39 »
13-15CAB G 52 »
13-15CAB H 52 »
13-15CAB H 53 »
13-15CNB G 110 »
13-15HG F 26.1 »
03.04.13-17HG E 22 »
J. Vogt, N. Davidson, G. Rätsch

Catalogue data

AbstractMachine 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.
ObjectiveThe 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.
ContentDuring 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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersJ. Vogt, N. Davidson, G. Rätsch
Typeungraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.
Additional information on mode of examinationAbsences 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.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

PriorityRegistration for the course unit is only possible for the primary target group
Primary target groupHuman Medicine BSc (377000)

Offered in

ProgrammeSectionType
Human Medicine BachelorCore Courses 3rd YearOInformation