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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.

History and structure of the Arabic language in its various forms. Graduate-level requirements include a research paper on any phonological, morphological, or syntactic structure of any variety of Arabic.

Students are introduced to computer programming as it pertains to collecting and analyzing linguistic data. The particular programming language is chosen at the discretion of the instructor. Graduate-level requirements include more challenging exams; 50% greater contribution to their respective group projects; 9 instead of 6 assignment; additional readings from the primary literature.

Neural networks are a branch of machine learning that combines a large number of simple computational units to allow computers to learn from and generalize over complex patterns in data. Students in this course will learn how to train and optimize feed forward, convolutional, and recurrent neural networks for tasks such as text classification, image recognition, and game playing.