- Schedule of Classes - June 25, 2020 7:14PM EDT
- Course Catalog - June 25, 2020 7:15PM EDT
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.
Regular Academic Session.
Credits and Grading Basis
4 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- MWHollister Hall B14
- Jan 21 - May 5, 2020
De Sa, C
Instruction Mode: Hybrid - Online & In Person
Disabled for this roster.