CSC585
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CSC585 - Algorithms for Natural Language Processing
Course Description
This course covers important algorithms useful for natural language processing (NLP), including distributional similarity algorithms such as word embeddings, recurrent and recursive neural networks (NN), probabilistic graphical models useful for sequence prediction, and parsing algorithms such as shift-reduce. This course will focus on the algorithms that underlie NLP, rather than the application of NLP to various problem domains.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Graduate
May be convened with
Name
Lecture
Workload Hours
3
Optional Component
No
Typically Offered Main Campus
Fall, Spring