Mathematical Foundations of Machine Learning

Catalog description

Machine Learning (ML) is a ubiquitous technology. This course, which is a follow up to an introductory course on ML will cover topics that aim to provide a theoretical foundation for designing and analyzing ML algorithms. This course has three basic blocks. First block will provide basic mathematical and statistical toolset required for formalizing ML problems effectively and analyzing them. This block will include topics like generalization, sample complexity of learning algorithm and understanding the inherent challenges in various ML frameworks and models. The second block will provide the foundations in algorithms design and optimization techniques required for building and analyzing various ML algorithms. This block will cover topics like gradient descent, stochastic gradient descent, algorithm design for online learning and computational challenges in ML. ML algorithms are deployed in real world and make decisions that affect real world users. The third block, will cover topics on how to formally reason about and how to design ML methods that address social and user related concerns that ML algorithms need to deal with. This block will cover topics such as fairness, privacy, the right to be forgotten and other such issues and how to build ML algorithms that address or assuage these concerns.