CS 4787

CS 4787

Course information provided by the Courses of Study 2019-2020.

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 Spring.

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

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Syllabi: none
  •   Regular Academic Session. 

  • 4 Credits Stdnt Opt

  • 12582 CS 4787   LEC 001

  • Instruction Mode: Hybrid - Online & In Person