CS 6787

CS 6787

Course information provided by the 2025-2026 Catalog.

Graduate-level introduction to system-focused aspects of machine learning, covering guiding principles and commonly used techniques for scaling up to large data sets. Topics will include stochastic gradient descent, acceleration, variance reduction, methods for choosing metaparameters, parallelization within a chip and across a cluster, and innovations in hardware architectures. An open-ended project in which students apply these techniques is a major part of the course.


Prerequisites CS 3780 or CS 4786.

Last 4 Terms Offered 2024SP, 2021FA, 2020FA, 2019FA

View Enrollment Information

Syllabi: none
  •   Regular Academic Session. 

  • 4 Credits Stdnt Opt

  • 16294 CS 6787   LEC 001

    • MW
    • Jan 20 - May 5, 2026
    • De Sa, C

  • Instruction Mode: In Person

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

Syllabi: none
  •   Regular Academic Session. 

  • 4 Credits Stdnt Opt

  • 18337 CS 6787   LEC 030

    • MW
    • De Sa, C

  • Instruction Mode: Distance Learning-Synchronous

    Enrollment limited to Cornell Tech PhD Students only.