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Prerequisite: COMP 250 with a minimum grade of "C-"; MATH 130, MATH 120 or MATH 202, and MATH 200 Machine Learning is the science of getting computers to act without being explicitly programmed and learn from experience; more specifically, its goal is to design algorithms that allow computers to learn from empirical data. Machine learning is an exciting interdisciplinary field, with historical roots in computer science, statistics, pattern recognition, and even neuroscience and physics. Many of these approaches have converged and led to rapid theoretical advances and real-world applications. This course will provide a broad introduction to the machine learning techniques that have proven valuable and successful in discovering patterns and making predictions in practical applications and students will be able to implement and apply these techniques on solving real problems. This course will also contrast the various methods, with the aim of explaining the circumstances under which each is most appropriate. The basic issues that confront any machine learning method will also be discussed. Offered alternate years.