Sanjeev Arora (Princeton University and Institute for Advanced Study)

September 11, 2018:

Lecture I 4:30-5:30 PM in SC Hall D

Lecture I:
"What is Machine Learning" 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 II:
"Toward Theoretical Understanding of Deep Learning" 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.

A reception follows the Tuesday lecture at 5:30 pm in
the Math Department common room.

This is a lecture series in honor of
Lars Ahlfors (1907-1996)
who was William Caspar Graustein Professor of Mathematics at
Harvard University from 1946 to 1977. Ahlfors won the fields medal in 1936 and
the Wolf prize in 1981.