INFO539
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INFO539 - 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. Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.
Min Units
3
Max Units
3
Repeatable for Credit
No
Grading Basis
GRD - Regular Grades A, B, C, D, E
Career
Graduate
Course Attributes
CE - CL (Cross Listed), GIDP - COGS (Cognitive Science), GIDP - SLAT (Sec. Lang. Acquisition & Teach), GIDP - STATD (Statistics and Data Science)
Cross Listed Courses
May be convened with
LING439
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