- Schedule of Classes - June 25, 2020 7:14PM EDT
- Course Catalog - June 25, 2020 7:15PM EDT
Course information provided by the Courses of Study 2019-2020.
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).
When Offered Spring.
Prerequisites/Corequisites 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 4780; CS 2110 or equivalent programming proficiency.
Regular Academic Session. Combined with: CS 5786
Credits and Grading Basis
4 Credits Stdnt Opt(Letter or S/U grades)
Class Number & Section Details
- TR Klarman Hall KG70
- Jan 21 - May 5, 2020
Instruction Mode: Hybrid - Online & In Person
Enrollment limited to CIS students only. All others may add themselves to the waitlist during add/drop. Please go to http://www.cs.cornell.edu/courseinfo/enrollment for updates. It is expected that undergraduate students enroll in the 4000-level section of this class and graduate students enroll in the 5000-level section of this class.
Disabled for this roster.