Theoretical Foundation of Machine Learning. 3 credits, 3 contact hours

Catalog description

Prerequisites: DS 675 . This course provides an advanced exploration of machine learning, emphasizing its mathematical foundations and the interplay between statistical and computational aspects. This course is designed for graduate-level students seeking to deepen their understanding of machine learning. It supports those aiming to contribute to learning theory and algorithm development. The course combines conceptual insights with quantitative analysis to build a strong foundation in the field. Key topics include classical results in generalization and optimization theory, recent advances in deep learning, unsupervised learning, and large language models (LLMs). Students will acquire tools to analyze and prove performance guarantees for learning methods, fostering a strong theoretical foundation and practical expertise in machine learning.