CS 4787

Principles of Large-Scale Machine Learning Systems

Course Description

Course information provided by the 2019-2020 Catalog.

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.

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

When Offered Spring.

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