LING 448

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Credit: 3 OR 4 hours.

Machine learning has been thriving in many areas for both research and industry. It offers solutions to problems that traditional approaches may not be able to deal with or fall short in efficiency. From unsupervised to supervised learning, this course provides a broad understanding of modern machine learning methods and techniques. Knowledge and skills are acquired in solving practical problems in clustering and classification, using techniques such as k-means, Gaussian mixture models, decision trees, support vector machines, and neural networks.

3 undergraduate hours. 4 graduate hours. Credit is not given toward graduation for LING 448 and either CS 441, 442, 446 or 545. Prerequisite: Intermediate-level Python programming skills. Students are expected to know how to use libraries and modules, basic data structures, and the concept of object-oriented programming.

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