CS 5786 - [Machine Learning for Data Science]

Spring. Not offered: 2021-2022. Next offered: 2022-2023. 4 credits. Student option grading.

Prerequisite: probability theory (BTRY 3080, ECON 3130, MATH 4710, or strong performance in ENGRD 2700 or equivalent), linear algebra (strong performance in MATH 2940 or equivalent), CS 2110 or equivalent programming proficiency. Co-meets with CS 4786.

Staff.

An introduction to machine learning for data-science applications. Topics include dimensionality-reduction (such as principal components analysis, canonical correlation analysis, and random projection); clustering (such as k-means and single-link); probabilistic modeling (such as mixture models and the EM algorithm). This course can be taken independently or in any order with CS 4780/CS 5780.