CS 5777

CS 5777

Course information provided by the Courses of Study 2022-2023.

An introduction to the mathematical and algorithms design principles and tradeoffs that underlie large-scale machine learning on big training sets. Topics include: stochastic gradient descent and other scalable optimization methods, mini-batch training, accelerated methods, adaptive learning rates, parallel and distributed training, and quantization and model compression.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: CS 4780 or CS 5780, CS 2110 or equivalents.

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Combined with: CS 4787

  • 4 Credits Stdnt Opt

  • 19134 CS 5777   LEC 001

  • Instruction Mode: In Person
    Direct enrollment is restricted to CS PhD, MS and MEng students. Seniors taking courses for M.Eng credit and all other graduate and professional students must add themselves to the waitlist during add/drop. Undergraduates who are not taking M.Eng credit will not be permitted and must enroll/waitlist for the 4xxx version. See website for details: http://www.cs.cornell.edu/courseinfo/enrollment/cs-4000-5000-level-courses