Machine Learning Essentials

Prof. Dr. Ullrich Köthe, SS 2022

Machine learning is one of the most promising approaches to address difficult decision and regression problems under uncertainty. The general idea is very simple: Instead of modeling a solution explicitly, a domain expert provides example data that demonstrate the desired behavior on representative problem instances. A suitable machine learning algorithm is then trained on these examples to reproduce the expert's solutions as well as possible and generalize it to new, unseen data. The last two decades have seen tremendous progress towards ever more powerful algorithms. This course attempts to cover all the essential methods from linear classifiers and robust regression to neural networks and reinforcement learning.

The lecture belongs to the Master of Data and Computer Science program, but is also recommended for students towards a Master of Physics (specialization Computational Physics), Master in Scientific Computing and anyone interested.

Solid knowledge in linear algebra, analysis (multi-dimensional differentiation and integration) and probability theory is required.

Dates:

Lecture Tuesdays 14:15-15:45 Room TBA
Lecture Fridays 11:15-12:45 Room TBA
Tutorials TBA
Please sign up for the lecture via Muesli.
(link will be activated in March 2023)

Homework assignments and other course material will be published on MaMPF MaMPF.