course: 2IIG0

This is the first data mining and machine learning course of CS&E bachelor students and it teaches the basics of the field. We start with the basics of optimization and cover then the two main paradigms of supervised and unsupervised learning. In particular, we introduce linear and non-linear regression, classification techniques, neural networks, clustering techniques, and matrix factorization (recommender system). The course offers the possibility to study the underlying theoretical foundations, to go deep in the theory of these models, as well as to gain practical application knowledge on real-world problems and datasets.

Details

Level:

Bachelor

Quartile:

Q2

Lecturers:

Sibylle Hess

Stiven Schwanz Dias