Machine Learning: my favorite
results, directions, and open problems
Avrim Blum, CMU
In this tutorial I will start from scratch and give an introduction to
some of the main themes and results in the theory of machine learning.
I will describe (a biased subset of) some of the main accomplishments
of computational learning theory to date, as well as current
directions and open problems. One of my goals in this tutorial is to
give my sense of why the theory of machine learning is a fundamental
part of the theory of computing, intimately connected to complexity
theory, cryptography, approximation algorithms, and online algorithms,
with a useful perspective that can be brought to bear in these other
areas. I will also talk a bit about the relation of theory and
practice in machine learning, and describe some current important
issues in the practice of machine learning where theoretical work may
be able to have substantial impact. Slides for this tutorial will be
available on the web at http://www.cs.cmu.edu/~avrim/Talks/FOCS03/.