CS 6787

Course information provided by the Courses of Study 2017-2018.

Graduate-level introduction to system-focused aspects of machine learning, covering guiding principles and commonly used techniques for scaling up to large data sets. Topics will include stochastic gradient descent, acceleration, variance reduction, methods for choosing metaparameters, parallelization within a chip and across a cluster, and innovations in hardware architectures. An open-ended project in which students apply these techniques is a major part of the course.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: CS 4780 or CS 4786.

View Enrollment Information

Enrollment Information
Syllabi: none
  •  

  • 4 Credits Stdnt Opt