- Schedule of Classes - June 2, 2019 7:14PM EDT
- Course Catalog - June 2, 2019 7:15PM EDT
Course information provided by the Courses of Study 2018-2019.
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 or equivalent, CS 2110 or equivalent.
Regular Academic Session.
Credits and Grading Basis
4 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- MWHollister Hall B14
De Sa, C
Enrollment limited to CIS students only. All others may add themselves to the waitlist during add/drop. Please go to http://www.cs.cornell.edu/courseinfo/enrollment for updates. It is expected that undergraduate students enroll in the 4000-level section of this class and graduate students enroll in the 5000-level section of this class.
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