ISTA439
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ISTA439 - Statistical Natural Language Processing
Course ID
019834
Course Description
This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Undergraduate
Course Attributes
CE - CL (Cross Listed)
Enrollment Requirements
018522
Course Requisites
Recommended completion of 1 course from: ISTA 350, CSC 345, DATA 375, LING 388 or LING 438, or equivalent.
Cross Listed Courses
May be convened with
LING539
Component
Lecture
Optional Component
No