CIS 526: MACHINE LEARNING
Spring 2006
Time: Wednesday,
4:40-7:20pm; Place: Tuttleman 305 A
Instructor: Zoran Obradovic
303 Wachman Hall,
Office hours: Wednesday 2-3pm and by appointment
Goals:
The goal of the field of
machine learning is to build computer systems that learn from experience and
that are capable to adapt to their environments. The course will cover the key algorithms and theory that form the core of
machine learning. Machine learning draws on concepts from many fields,
including statistics, artificial intelligence, philosophy, information theory,
biology, cognitive science, computational complexity, and control theory. This
course should provide a view from all of these perspectives and facilitate
understanding of different problem settings, algorithms and assumptions that
underlie each. The course will include individual projects to provide the
students a better insight in the machine learning research and potentially
attract them to pursue the graduate research in this area.
Prerequisites:
Stat503 or CIS511, undergraduate understanding of
probability, statistics, linear algebra, calculus.
Study Materials:
Primary textbook: Mitchell,T.M. Machine
Learning, WCB/McGraw-Hill,1997,ISBN 0-07-042807-7.
Recommended book: Duda R.O., Hart P.E., Stork
D.G. Pattern Recognition, Wiley &
Sons, 2001, ISBN 0-471-05669-3
Conference and journal papers will be used as
supplemental materials.
Topics: Content will
include:
·
Learning system
design and evaluation
·
Decision tree
learning
·
Neural networks, support
vector machines and ensemble methods
·
Unsupervised
learning, clustering and density estimation
·
Dimensionality
reduction
·
Bayesian networks
·
Reinforcement
learning.
Grading: Homework (30%), midterm exam (20%), reading/presenting assignments
(20%) and an individual research project (30%).
Late Policy and Academic Honesty: No late submissions will be
accepted. Discussing materials with fellow students is acceptable, but
programs, experiments and the reports must be done individually.