Monday 10.00-12.00, Arnimallee 6 (Pi-Building), SR 007/008
There will be an additional seminar on
January 31 (Wednesday), 14:00-16:00
Room A7/SR 031
Machine learning or, more generally, artificial intelligence is nowadays ubiquitous. Explicitly or implicitly, it surrounds us, hiding behind anything, ranging from smartphones and social networks to self-driving vehicles. Machine learning deals with searching for and generating patterns in data. Although it is traditionally considered a branch of computer science, it heavily relies on mathematical foundations. Thus, it is the primary goal of our seminar to understand these mathematical foundations. In doing so, we will mainly follow the classical monograph  and combine the two complementary viewpoints: deterministic and probabilistic. In this semester, we will focus on artificial neural networks, graphical models, and latent variables approach. All these machine learning methods are widely used nowadays and still belong to the fields of active research.
Doing exercises (that are present in  in abundance) and programming is beyond the seminar’s scope. However, the students are very much encouraged to do both on their own.
Interested students are supposed to be acquainted with basics of probability theory and linear deterministic and probabilistic models for regression and classification, see, e.g., [1, Chapters 2-4, 6, 7].
The language of the seminar is English.
The topics will be assigned during the first seminar.
In the list of (most) topics below, the numbers in brackets refer to the corresponding sections in . As a complement, the monographs [2,3,4] are recommended.
Artificial neural networks:
Discrete latent variables: clustering, mixture models and expectation maximization (EM)
Continuous latent variables: principal component analysis (PCA)