Topics in Machine Learning
Computing Science 651
First Semester, 1998-99
Time: TTh 12:30-2:00
Place: GSB 711
Intructor: Russ Greiner
LectureNotes
Evaluation Textbooks
Resources NewsGroup
Outline
Bi-weekly meetings with teams on Fridays:
9/Oct
23/Oct
6/Nov
27/Nov
Purpose
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
cover
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practical aspects, including algorithms for learning decision trees, neural
networks and belief networks;
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general models, including genetic algorithms and reinforcement learning;
and
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theoretical concepts, including relevant ideas from statistics, inductive
bias, Bayesian learning and the PAC learning framework.
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.
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.
Prerequisite
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.
Textbooks
Course Outline (1998
topics)
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Introduction
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Decision Tree Learning
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Computational Learning Theory
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Evaluating Hypotheses
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Artificial Neural Networks
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Bayesian Learning + Learning "Belief Nets"
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Reinforcement Learning
If time permits...
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Instance-Based Learning (including RadialBasisFunctions)
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Genetic Algorithms + Genetic Programming
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Learning Sets of Rules
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Combining Inductive and Analytical Learning
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New results: Boosting, Exponentiated Gradient, Support Vector Machine,
...
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New applications: Datamining, Adaptive Software, Computational Molecular
Biology, ...
Lecture
Notes
To contact others in the course (including students, auditors and the
prof), check
news:ualberta.courses.cmput.651
Evaluation:
Late Policy: No late assignments or papers will be accepted.
Office Hours:
Machine Learning Resources on the Web
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98 -- Soln