Topics in Machine Learning
Computing Science 651
First Semester, 1998-99
Time: TTh 12:30-2:00
Place: GSB 711
Intructor: Russ Greiner
Bi-weekly meetings with teams on Fridays:
Learning -- ie, using experience to improve performance -- is an
essential component of intelligence. The field of Machine Learning,
which addresses the challenge of producing machines that can
learn, has become an extremely active, and exciting area, with an ever
expanding inventory of practical (and profitable) results, many enabled
by recent advances in the underlying theory.
This course provides a graduate-level introduction to the field, with
an emphasis on the design on agents that can learn about their environment,
to help them improve their performance on a range of tasks. We will
Programming assignments will include hands-on experiments with various
learning algorithms, possibly including neural network learning for face
recognition, and decision tree learning from databases of credit records.
practical aspects, including algorithms for learning decision trees, neural
networks and belief networks;
general models, including genetic algorithms and reinforcement learning;
theoretical concepts, including relevant ideas from statistics, inductive
bias, Bayesian learning and the PAC learning framework.
If time permits, we will also survey the latest new results (boosting,
exponentiated gradient, support-vector machines, ...) and discuss some
new applications, in the areas of data-mining, adaptive software systems,
and computational molecular biology.
Familarity with Artificial Intelligence (eg, CMPUT 451/551 or equivalent);
knowledge of Lisp, Prolog helpful. Students who are interested in
the material but do not have the required prerequisite are encouraged to
talk to the instructor.
Course Outline (1998
Decision Tree Learning
Computational Learning Theory
Artificial Neural Networks
Bayesian Learning + Learning "Belief Nets"
If time permits...
Instance-Based Learning (including RadialBasisFunctions)
Genetic Algorithms + Genetic Programming
Learning Sets of Rules
Combining Inductive and Analytical Learning
New results: Boosting, Exponentiated Gradient, Support Vector Machine,
New applications: Datamining, Adaptive Software, Computational Molecular
To contact others in the course (including students, auditors and the
Late Policy: No late assignments or papers will be accepted.
Machine Learning Resources on the Web
To track down other literature
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