Module Number ML-4202 |
Module Title Probabilistic Machine Learning (Probabilistic Inference and Learning) |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Workload:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Probabilistic inference is a foundation of scientific reasoning, statistics, and |
|
Objectives | Students gain an intuitive, as well as a mathematical and algorithmic understanding |
|
Allocation of credits / grading |
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
4
6.0
wt
90
g
100
Tutorial
Ü
o
2
3.0
|
|
Prerequisite for participation | ML-4101 Mathematics for Machine Learning | |
Lecturer / Other | Hennig, Macke | |
Literature | Literature will be listed at the beginning of the semester. / Standard undergraduate knowledge of mathematics is required, to the extent |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2025 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV, ML-FOUND |