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CMPT 820

Topics in Learning and Intelligent Systems

3 credits · Graduate

This course explores advanced techniques for management and analysis of data in unstructured application environments. Techniques covered may be chosen from the following: Bayesian modelling, data conditioning, machine learning (Bayesian inference, neural networks, decision trees, classifiers), user interface agents, and other similar techniques in the AI research literature as appropriate.

Prerequisites & course information

Weekly hours: 3 Lecture hours Prerequisite(s): Open to graduate students in computer science who have at least one undergraduate course (3 credit units) of Artificial Intelligence.

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