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| CS 57800 - Statistical Machine Learning | |||||||||
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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 Syllabus Available Levels: Undergraduate, Graduate, Professional Schedule Types: Lecture Offered By: College of Science Department: Computer Science Course Attributes: Upper Division May be offered at any of the following campuses: West Lafayette 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. Restrictions: Must be enrolled in one of the following Levels: Graduate Prerequisites: GR-CS 57800 Requisites General Requirements: ( Student Attribute: GR May not be taken concurrently. ) or ( Course or Test: MA 16200 Minimum Grade of D- May not be taken concurrently. ) or ( Course or Test: MA 35100 Minimum Grade of D- May not be taken concurrently. ) or ( Course or Test: STAT 41600 Minimum Grade of D- May not be taken concurrently. ) Short Title: Statistical Machine Learning Course Configurations:
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