Welcome to CS243:
Topics in Machine Learning
Date and Time: Wednesdays, 3:00-5:00pm in CIT Room 506
Instructors: Tom
Dean with help from Kee-Eung Kim
and Luis Ortiz
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Index:
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Introduction
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Syllabus and Handouts
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Textbook and Supplementary Readings
Introduction
This course covers machine learning from the AI perspective. We consider
different learning problems, including concept learning, clustering, and
reinforcement learning. For each learning problem, we investigate a
variety of proposed solutions, including those from outside of mainstream
AI, such as artificial neural networks and standard statistical methods.
Students are expected to design an empirical learning experiment involving
replication of published results, comparison of existing algorithms, or
comparison of an original algorithm with existing algorithms, then carry
out and report on the results of the experiment. This course is aimed at students interested in
learning about the basic practical and theoretical issues in machine
learning.