Content

The Bayesian perspective on probability is a cornerstone of modern applied statistics and probabilistic machine learning. Probabilistic models formulated in this framework allow to explicitly communicate and challenge assumptions, perform consistent reasoning, and quantify the uncertainty of predictions — they are a useful tool in data-driven research as well as decision-making.

This lab course teaches how such models can be implemented in Python — using commonly used modeling packages — and fit to data for Bayesian inference, uncertainty quantification and prediction.

The focus of the course will be on basic Bayesian models, but will also touch upon Bayesian neural networks.

There exists a seminar of the same name which synergizes well, particularly if there has not been previous exposure to probability.

Learning Outcomes

After successful completion, students will

• Be able to implement basic Bayesian models in Python

• Will be able to visualise, diagnose and interpret these models

Learning Methods

Hands-on lab course during the semester

Exam

Practical exam

Requirements for awarding of Credit Points

Successful participation in the lab course.

Attendance required. Missing for more than one session will without a medical certificate will result in a „Fail“ for this course.

Dr. Merlin Schüler Lecturer

merlin.schueler@ini.rub.de NB 3/34

Course type

Lab courses

Credits

3

Term

Summer Term 2026

Lab course

Takes place every week on Tuesday from 14:15 to 17:00 in room MB 2/90.
First appointment is on 14.04.2026
Last appointment is on 21.07.2026

Mathematical foundations as taught in the first three semesters of the study programs of the Faculty of Computer Science.

There exists a seminar of the same name which synergizes well, particularly if there has not been previous exposure to probability.

We expect fluency in one other programming language and familiarity with concepts like

• control structures

• data types

• functions

• object-oriented programming

These concepts will not be taught separately.

Furthermore, the course will be taking place in a room without PCs, meaning that students are required to use their own laptops during the course.

Literature:
„Statistical Rethinking“ (2nd edition) by Richard McElreath