Machine learning studies the question “how can we build computer
programs that automatically improve their performance through
experience?” This includes learning to perform many types of tasks
based on many types of experience. For example, it includes robots
learning to better navigate based on experience gained by roaming
their environments, medical decision aids that learn to predict which
therapies work best for which diseases based on data mining of
historical health records, and speech recognition systems that learn
to better understand your speech based on experience listening to
you.
This course is designed to give PhD students a thorough grounding in
the methods, theory, mathematics and algorithms needed to do research
and applications in machine learning. The topics of the course draw
from machine learning, classical statistics, data mining, Bayesian
statistics and information theory. Students entering the class with a
pre-existing working knowledge of probability, statistics and
algorithms will be at an advantage, but the class has been designed so
that anyone with a strong numerate background can catch up and fully
participate.
Resources
For specific videos of the class, as well as slides, go to the individual lectures in the schedule below or the menu at left.
This is also where you'll find pointers to further reading material etc.