Topics in Machine Learning

Computing Science 651
First Semester, 1998-99

Time: TTh 12:30-2:00
Place: GSB 711
Intructor: Russ Greiner

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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

    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.


    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 topics)

    1. Introduction
    2. Decision Tree Learning
    3. Computational Learning Theory
    4. Evaluating Hypotheses
    5. Artificial Neural Networks
    6. Bayesian Learning + Learning "Belief Nets"
    7. Reinforcement Learning

    8. If time permits...
    9. Instance-Based Learning (including RadialBasisFunctions)
    10. Genetic Algorithms + Genetic Programming
    11. Learning Sets of Rules
    12. Combining Inductive and Analytical Learning
    13. New results: Boosting, Exponentiated Gradient, Support Vector Machine, ...
    14. New applications: Datamining,  Adaptive Software, Computational Molecular Biology, ...
    Lecture Notes

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    Late Policy: No late assignments or papers will be accepted.

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