Introduction to Probabilistic Reasoning and Numerical Methods in Machine Learning

This course will introduce the basic concepts of reasoning under uncertainty, as well as provide an overview of common methods for data processing, optimization, and machine learning. After an introduction to probability theory and common probability distributions, we discuss inference tasks with various probabilistic models and continue by outlining methods to approach more involved inference tasks through approximation or sampling. From there we continue by presenting the basics of data de-correlation, reduction, and optimization algorithms. We continue with classical machine learning topics, such as clustering, classification, and regression with tree-based- and neural-network-algorithms. In the end, several deep learning models and architectures will be introduced and discussed.

Linear Algebra, basic Analysis, a programming language of choice recommended.