Stas Syrota

Stas Syrota

Trying to fix Bayesian Deep Learning during my PhD at DTU supervised by Søren Hauberg.

Teaching

Introduction to Machine Learning and Data Mining

Master's level course, Technical University of Denmark, Department of Mathematics and Computer Science, 2023

Content of the course

principal component analysis. Similarity measures and summary statistics. Visualization and interpretation of models. Overfitting and generalization. Classification (decision trees, nearest neighbor, naive Bayes, neural networks, and ensemble methods.) Linear regression. Clustering (k-means, hierarchical clustering, and mixture models.) Association rules. Density estimation and outlier detection. Applications in a broad range of engineering sciences.

Bayesian Machine Learning

Master's level course, Technical University of Denmark, Department of Mathematics and Computer Science, 2023

Description of the course

The purpose of the course is two-fold. First of all, the goal is to equip students with a deeper theoretical understanding of probabilistic machine learning and to enable them to read and understand the newest research literature in the field. Second, to enable students to discuss probabilistic models for practical problems and to discuss and apply appropriate inference algorithms.