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Artificial Intelligence and Machine Learning

Recent breakthroughs in Artificial Intelligence (“AI”) and Machine Learning (“ML”) are changing many industries, with the sports industry being no exception.  With the sports world embracing data-driven decision making, the demand has never been higher for AI/ML.  Through an emphasis on understanding the concepts underlying AI and ML, this course seeks to demystify these important techniques.  Topics include machine learning (supervised and unsupervised); AI, deep learning, and computer vision; natural language processing; and Python. Students who enjoy sports statistics will be encouraged throughout the course to independently use the data in the sport of their choice to analyze and apply concepts discussed in class.
Students pursuing the Certificate in Sports Technology and Innovation are required to complete Foundations in Digital Disruption/TGSC1-CE1007 as part of the certificate. However, there is no required order of completion for courses in the program as long as Foundations in Digital Disruption is one of the four earned toward the certificate.  For questions on the Certificate in Sports Technology and Innovation, please contact tischinstitute.ce@nyu.edu.  For assistance with registration, please call 212-998-7150.
 

More details

You'll Walk Away with

  • An understanding of the concepts underlying Machine Learning and Artificial Intelligence.
  • The ability to use Python to apply AI/ML techniques to sports data.
  • The ability to understand and discuss how an AI/ML technique can provide insights to issues in the sports industry.

2 sections

  • Fall 2024

    • Section

      001
    • Semester

      Fall 2024
    • Date

      Sep 3 - Dec 12
    • Day

      Self-Paced
    • Time

      Self-Paced
      • Online
    • Format

      Online
      • Online
    • Sessions

      N/A
    • Faculty

      TBA
    • Location

      Self-Paced
    Website registration is not available. Please call (212) 998-7150 for more information on how to enroll.
  • Spring 2025

    • Section

      001
    • Semester

      Spring 2025
    • Date

      Jan 27 - May 6
    • Day

      Self-Paced
    • Time

      Self-Paced
      • Online
    • Format

      Online
      • Online
    • Sessions

      N/A
    • Faculty

      Hall, Brian
    • Location

      Self-Paced