Machine
Learning / CS536 / Spring 2008
Class
Lectures: Tue, 3:20 - 6:20, Hill 254
Instructor
Vladimir Pavlovic
Email Vladimir Pavlovic
Office: CoRE 312
Office hours: Thu, 2:00 - 5:00
Phone: 732 445 2654
Teaching Assistant
Pavel Kuksa
Email Pavel Kuksa
Office: Hill 270
Office hours: Wed, 3:00 - 5:00
Course Description
An in-depth study of machine learning, to impart an
understanding of the major topics in this area, the
capabilities and limitations of existing methods, and
research topics in this field.
Topics
Inductive learning, including decision-tree and
neural-network approaches, Bayesian methods, computational
learning theory, instance-based learning, explanation-based
learning, reinforcement learning, nearest neighbor methods,
PAC-learning, inductive logic programming, genetic
algorithms, unsupervised learning, linear and nonlinear
dimensionality reduction, kernels methods, graphical
models, regression modeling.
Expected Work
Regular readings; mini-projects; in-class presentations;
midterm and a final course project.
Textbooks
"Pattern Recognition and Machine Learning" by
Christopher M. Bishop, Springer, 2006.
"Introduction to Machine Learning" by Ethem Alpaydin, The
MIT Press, October 2004.
See this for more details.
Software
We will use MATLAB extensively! Follow the
link for more details.
Course Policies and
Procedures
Important, perhaps boring details. But please read
them carefully.
Schedule
Schedule of topics, assignments, and
tests.