CS 4786

Course information provided by the Courses of Study 2015-2016.

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.

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 2110 or equivalent programming proficiency.

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Enrollment Information
Syllabi: none
  •   Combined with: CS 5786

  • 4 Credits Stdnt Opt

  • 12431CS 4786  LEC 001

  • Enrollment open only to CIS students. (CIS graduate students should enroll in CS 5786). All others may add themselves to the waitlist. Please go to http://www.cs.cornell.edu/courseinfo/enrollment for updates.