• Course Number: 16:198:536
  • Course Type: Graduate
  • Semester 1: Spring
  • Credits: 3
  • Description:

    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.

  • M.S. Course Category: AI/Machine Learning
  • Category: A or B (M.S.), B (Ph.D.)
  • Prerequisite Information:

    16:198:530 or 16:198:520

  • Course Links: 16:198:520 - Introduction To Artificial Intelligence, 16:198:530 - Principles of Artificial Intelligence
  • 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.