CSE244: Machine Learning for Natural Language Processing

Introduction to machine learning models and algorithms for Natural Language Processing. Covers deep learning approaches and  traditional  machine learning models. Topics include an introduction to standard neural network learning methods such as feed-forward neural networks, recurrent neural networks, convolutional neural networks, and encoder-decoder models with applications to natural language processing problems such as utterance classification and sequence tagging. Requirements include a midterm, final, programming assignments, and a project. Enrollment is restricted to graduate students in the computer engineering and computer science master's programs; and students in the following doctoral programs: computer engineering, computer science, applied mathematics, statistical science, biomolecular engineering and bioinformatics, electrical engineering, and technology information management. Others may enroll by permission of the instructor.

5 Credits


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