Psychology | Machine Learning
P657 | 24646 | Yu, C
P657 Machine Learning
Instructor: Chen Yu
Term: Spring, 2004-2005
Time: 10:00am - 12:00pm W
The goal of artificial intelligence has always been to imitate human
intelligence but the methodology has changed radically. This seminar
is intended to provide an introduction to machine learning
techniques. We will explore how these techniques could be used to
not only simulate human intelligence but also potentially make
predictions about aspects of natural intelligence.
Topics: probability theory, optimization theory, dynamic systems,
principle component analysis/eigenface, Bayes Classifiers, k-
means/hierarchical clustering, Gaussian mixture models/expectation-
maximization, support vector machines, backpropagation, self-
organizing map, dynamic programming, hidden Markov models/speech
recognition, Gibbs sampling and Markov Chain Monte Carlo, game
theory, and Kalman filter.
Goals: Time constraints will prevent coverage in depth. The aim will
be to introduce and survey some of state-of-art techniques in
machine learning, so that students will be stimulated to continue
learning and applying machine learning techniques to their own
Format: The instructor will give lectures on most topics. Students
will need to present at least one paper and lead the following
Requirements: In addition to presentations and participation in
discussion, students will be responsible for a term project. The aim
will be to apply machine learning techniques to original research.
The instructor will discuss with individual students about their
projects. Students will need to write a formal report as well as
present their work in class at the end of semester.
Contact: If you have any questions, please contact Dr. Chen Yu, 856-