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Course Instructor: Monson H. Hayes

Course Time: Thursday, 4:30 - 7:10 PM

Office Hours: 19:00-20:30 and by appointment.

 Y. Abu-Mostafa, M. Magdon-Ismail and H-T. Lin, Learning from Data, AML Press, 2012

Prerequisites: The student is expected to have an understanding of basic probability The student should also have a strong background in mathematics, particularly linear algebra.  An understanding of optimization theory would be beneficial, but not necessary. The most important prerequisite, however, is the desire to learn and work outside of class to explore new ideas and applications.

Course Objectives:

  1. To understand what it means for a machine to learn.
  2. To know when learning is possible
  3. To be able to use machine learning tools and algorithms to make learning feasible.

Course Requirements
Homework and computer exercises.
Course project with a presentation.

Click Here for PDF of the Syllabus