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|CS 57800 - Statistical Machine Learning|
Credit Hours: 3.00. This introductory course will cover many concepts, models, and algorithms in machine learning. Topics include classical supervised learning (e.g., regression and classification), unsupervised learning (e.g., principle component analysis and K-means), and recent development in the machine learning field such as variational Bayes, expectation propagation, and Gaussian processes. While this course will give students the basic ideas and intuition behind modern machine learning methods, the underlying theme in the course is probabilistic inference. Typically offered Fall.
3.000 Credit hours
Levels: Graduate, Professional, Undergraduate
Schedule Types: Lecture
Offered By: College of Science
Department: Computer Science
May be offered at any of the following campuses:
Repeatable for Additional Credit: Yes - May be repeated up to 2 times
Learning Outcomes: 1. Have students prepared to either conduct PhD research in the machine learning area or apply it to other application domain, such as computational biology, materials science and social science.