Mathematical and Statistical Foundations of Machine Learning
Course Description
Due to its remarkable success in a wide range of applications, machine learning plays an increasingly prominent role in the intersection of mathematics, statistics, and data science. Consequently, it becomes more and more important to understand and develop theory that supports further advancement in machine learning. This course provides the fundamental concepts and frameworks in machine learning, including PAC learning framework, Rademacher complexity, VC-dimension, empirical risk minimization, support vector machine, kernel methods, and more.
Prerequisites
Participants are expected to have knowledge of basic probability (WP3 Stochastics). Additional knowledge of advanced probability and analysis (WP20 Probability Theory) is also highly recommended.
Schedule and Venue
- Lectures: Tue 10:00–12:00 (B047) and Thu 10:00–12:00 (B047)
- Exercises: Slot A on Mon 14:00–16:00 (B132) and Slot B Mon 16:00–18:00 (B132). Please choose one slot.
Creditable Modules
- MSc Mathematik (2011, 2021) WP5 Mathematische Statistik 9 ECTS
- MSc Finanz- und Versicherungsmathematik (2019) WP21 Mathematische Statistik 9 ECTS
- MSc Finanz- und Versicherungsmathematik (2021) WP20 Elective Topics in Statistics and Probability 9 ECTS