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CMPUT 466/551 Description

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 (near)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

  • practical aspects, including algorithms for learning decision trees, neural networks and  belief networks;
  • general models, possibly including reinforcement learning;  and
  • 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 (exponentiated gradient, ...) and discuss some new applications, in the areas of data-mining, and computational molecular biology.
See Lecture Notes for more details.

It is intended for 4th year undergrads, and 1st year MSc (and perhaps PhD) students.