Information Geometry and Its Applications in Machine Learning and Beyond

Catalog description

This course offers an advanced, research-oriented exploration of Information Geometry and its significant applications across Machine Learning, Statistics, and various real-world domains. Information Geometry provides a mathematical framework to understand the underlying geometry of probability distributions and how it impacts learning algorithms, statistical models, and other advanced topics in computational fields. The course aims to equip students with both theoretical knowledge and practical tools to investigate cutting-edge research questions. | Prerequisites: Matriculation as a PhD student or permission of the FRE Department