Lecture 1: Mathematics of Machine Learning: An introduction
Date & Time: Tuesday, 12 February, 11:30
Abstract: Machine learning is the sub-field of computer science concerned with creating programs and machines that can improve from experience and interaction. It relies upon mathematical optimization, statistics, and algorithm design. The talk will be an introduction to machine learning for a mathematical audience. We describe the mathematical formulations of basic types of learning such as supervised, unsupervised, interactive, etc., and the philosophical and scientific issues raised by them.
Lecture 2: Toward theoretical understanding of deep learning
Date & Time: Tuesday, 12 February, 15:00
Abstract:The empirical success of deep learning drives much of the excitement about machine learning today. This success vastly outstrips our mathematical understanding. This lecture surveys progress in recent years toward developing a theory of deep learning. Works have started addressing issues such as speed of optimization, sample requirements for training, effect of architecture choices, and properties of deep generative models.
Lecture 3: Theoretical analysis of unsupervised learning
Date & Time: Wednesday, 13 February, 11:30
Abstract:Unsupervised learning refers to learning without human-labeled datapoints. This can mean many things but in this talk will primarily refer to learning representations (also called embeddings) of complicated data types such as images or text. Empirically it is possible to learn such representations which have interesting properties and also lead to better performance when combined with labeled data. This talk will survey some attempts to theoretically understand such embeddings and representations, as well as their properties. Many examples are drawn from natural language processing.
Participation is by registration only. Registration is only for Bangalore participants and we welcome participants from academia and industry.
