CS 6787

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CS 6787

Course information provided by the Courses of Study 2023-2024.

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.

When Offered Spring.

Prerequisites/Corequisites Prerequisite: CS 4780 or CS 4786.

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Choose one lecture and one project.

  • 4 Credits Stdnt Opt

  • 19523 CS 6787   LEC 001

  • Enrollment is restricted to graduate students only. All others must add themselves to the waitlist during add/drop in January.

  • 19524 CS 6787   PRJ 601

    • TBA
    • Jan 22 - May 7, 2024
    • De Sa, C

Syllabi: none
  •   Regular Academic Session.  Choose one lecture and one project.

  • 4 Credits Stdnt Opt

  • 20421 CS 6787   LEC 030

  • Instruction Mode: Distance Learning-Synchronous
    Taught in NYC at Cornell Tech. Enrollment Limited to Cornell Tech PhD Students only.

  • 20422 CS 6787   PRJ 602

    • TBA Cornell Tech
    • Jan 22 - May 7, 2024
    • De Sa, C

  • Instruction Mode: Distance Learning-Synchronous
    Taught in NYC at Cornell Tech. Enrollment Limited to Cornell Tech PhD Students only.