CS 4786

CS 4786

Course information provided by the Courses of Study 2017-2018.

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 Fall.

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.

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Combined with: CS 5786

  • 4 Credits Stdnt Opt

  • 12976 CS 4786   LEC 001

  • 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.