Module Number

ML-4502 		Module Title

Machine learning methods for scientific discovery 		Lecture Type(s)

Seminar
ECTS 3
Work load
- Contact time
- Self study
Workload:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency Irregular
Language of instruction English
Type of Exam

Oral presentation, written report
Content

In this seminar, we will discuss current and classical research papers which
describe machine learning methods for applications in the natural sciences.
From a methodological perspective, a particular focus will be on ‘simulationbased
inference approaches’, as these provide a bridge between data-driven
machine learning methods, and theory-driven scientific modelling, as well as on
latent-variable models for inferring dynamical systems from data.
Objectives

Students are able to read and reflect upon current research papers in this
research area. They can critically assess the contributions of such a paper. They
can present current research results to other students and researchers and can
lead research discussions. They can summarize and evaluate the results of a
paper in form of a written research report.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Seminar
S
o
2
3.0
tp, op
30
g
199
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Macke
Literature

Will be announced in the first meeting / Basic knowledge probabilistic machine learning
Last offered Wintersemester 2021
Planned for Sommersemester 2023
Assigned Study Areas INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV