ISTA439
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ISTA439 - Statistical Natural Language Processing
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
Cross Listed Courses
May be convened with
LING539
Component
Lecture
Optional Component
No
Typically Offered Main Campus
Fall
Typically Offered Distance Campus
Not Offered
Typically Offered Online Campus
Not Offered
Typically Offered Phoenix Campus
Not Offered
Typically Offered Sierra Vista Campus
Not Offered
Typically Offered Community Campus
Not Offered