The following is an incomplete list of machine learning courses offered at CU. 

The prefix codes for courses are CSCI (computer science), APPM (applied math), STAT (statistics, taught by the applied math department), and ECEN (electrical engineering).


  • STAT 2600: Introduction to Data Science and Machine Learning
  • CSCI 5352: Network Analysis and Modeling
  • CSCI 5622: Machine Learning
  • CSCI 5822: Probabilistic Models
  • CSCI 5922: Neural Networks and Deep Learning
  • STAT 4610: Statistical Machine Learning (aka Statistical Learning)


  • CSCI 7000, APPM 4490/5490: Theoretical Machine Learning. Tentatively taught every spring (alternating years by CS or APPM, currently as special topics courses); uses Shalev-Shwartz and Ben-David book, as well as supplements from Mohri et al.
  • CSCI 6622: Advanced Machine Learning.  Semester-long research projects course; reading and discussing research articles from the academic literature. Prerequisite: CSCI 5352, 5622, 5822, or 5922.
  • CSCI 6314: Algorithmic Economics.  Typically covers topics at the intersection of microeconomics and theoretical machine learning (strategically robust learning, algorithmic fairness, etc).
  • ASEN 6519: Algorithms for Aerospace Autonomy
  • APPM 4/5515 High-Dimensional Probability for Data-Science
  • ECEN 5712 Machine Learning for Engineers. Tentatively taught every spring (by Profs. Liu or Varanasi), uses Shalev-Shwartz and Ben-David book
  • APPM 4720/5720: Applied Deep Learning (Parts 1 & 2)