STSCI 6840

Last Updated

  • Schedule of Classes - April 13, 2026 10:10AM EDT

STSCI 6840
Mathematics of Statistical Learning

Course information provided by the 2026-2027 Catalog.

Learning theory is an important branch of modern statistics. This course gives an overview of various topics and proof techniques that include concentration inequalities, Bayes rules, reject option, margin condition, local averaging methods, universal consistency, empirical risk minimization, convex surrogate losses, Rademacher complexity, VC theory, structural risk minimization, sparse methods, low-rank regression, topic models, latent factor models and interpolation methods.


Prerequisites MATH 6710 and STSCI 6730, or permission of instructor.

Enrollment Priority Enrollment limited to: graduate students.

Last 4 Terms Offered (None)

Learning Outcomes

  • Students will familiarize themselves with general results in Learning Theory
  • Students will get acquainted with the proof techniques used in Learning Theory

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Syllabi: none

  •   Regular Academic Session. 

  • 3 Credits Stdnt Opt

  • 18628 STSCI 6840   LEC 001

    • MW
    • Aug 24 - Dec 7, 2026

    • Wegkamp, M

  • Instruction Mode: In Person

    For Bowers Computer and Information Science (CIS) Course Enrollment Help, please see: https://tdx.cornell.edu/TDClient/193/Portal/Home/

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