Introduction to Bayesian modeling

Registration

Please send an eMail to merlin.schueler@ini.rub.de until April 10th, 2025.
In this eMail, include your study program (e.g. Masters CogSci) and possible previous experience on the topic.

Students of the computer science faculty can not take this course in summer semester 2025.

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 seminar aims to explore the conceptual foundations of building these models and employ them for statistical inference and is meant for students without significant prior exposure to the topic.

Teaching style

The course is based on chapters of book „Statistical Rethinking“ by Richard McElreath with required reading and in-session discussion.

Exam

The grade will be determined by participation and the presentation of a topic during the course of the seminar.

Lecturers

Details

Course type
Seminars
Credits
3
Term
Summer Term 2025

Dates

Seminar
Takes place every week on Monday from 10:15 to 11:45 in room NB 3/72.
First appointment is on 14.04.2025
Last appointment is on 14.07.2025

Requirements

Some familiarity with mathematical notation is required. 

However, the focus is on intuition and application instead of mathematical rigor.

About the INI

The Institut für Neuroinformatik (INI) is a research unit of the Faculty of Computer Science at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de