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Course Description

An overview of methods whereby computers can learn from data or experience and make decisions accordingly. Topics include supervised learning,unsupervised learning, reinforcement learning, and knowledge extraction from large databases with applications to science,engineering, and medicine.Prerequisites:  COMP 15 and MATH/COMP 61  or graduate standing. COMP 160 highly recommended.

Affiliated With:

  • School of Engineering