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Machine Learning

16:198:536

An in-depth study of machine learning, to impart an understanding of the major topics in this area, the capabilities and limitations of existing methods, and research topics in this field.

Credits: 
3
Category: 
B (M.S.)
B (Ph.D.)
Prerequisite: 
Semesters: 
Spring
Topics: 

Inductive learning, including decision-tree and neural-network approaches, Bayesian methods, computational learning theory, instance-based learning, explanation-based learning, reinforcement learning, nearest neighbor methods, PAC-learning, inductive logic programming, genetic algorithms, unsupervised learning, linear and nonlinear dimensionality reduction, and kernels methods.

Expected Work: 

Regular readings; occasional assignments; in-class presentations; midterm and final examination and/or a course project.

Teaching Professors Names: 
Ahmed Elgammal