Course details



L + T
WS 2023 Prof. Dr. Pascal Nieters OFFLINE
B.Sc modules:
CS-BP-NI - Neuroinformatics
CS-BWP-NI - Neuroinformatics
KOGW-PM-NI - Neuroinformatics
KOGW-WPM-NI - Neuroinformatics

CS-BW - Bachelor elective course
CS-MW - Master elective course
Thu: 14-16
Fri: 10-12
Fri: 12-14

Prerequisites: Applied Mathematics; or Linear Algebra, Analysis. In this course, we will discuss innovative and foundational methods in the field of Neuroinformatics. The goal of the lecture is to familiarise students with modelling and interpreting data in the context of state-of-the-art knowledge about computational processes in the brain. After an introduction to probability theory and linear models for regression and classification, we will take a journey through graphical models, model selection and Bayesian modelling. To connect methods to scientific questions, we will regular detours into research topics in neuroinformatics. The lecture is proposed for students in the Bachelor's course in their third semester as a compulsory module, and for students in the Master's course as an elective.