CSC 588 Machine Learning Theory

Students will learn how and when machine learning is possible/impossible as well as various algorithms with theoretical guarantees under minimal assumptions. Specifically, the course offers formulation of learning environments (e.g., stochastic and adversarial worlds with possibly limited feedback), fundamental limits of learning in these environments, various algorithms concerning sample efficiency, computational efficiency, and generality. Throughout, students will not only learn fundamental tools upholding the current understanding of machine learning systems in the research community but also develop skills of adapting these techniques to their own research needs such as developing new algorithms for large-scale, data-driven applications.