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CMPUT 466: Machine Learning Essentials

At a glance

  • Units: 3
  • Prerequisites: CMPUT 204 or CMPUT 275; any 300-level Computing Science course; MATH 102, 125, 126, or 127; one of MATH 115, 118, 136, 146, or 156; and one of STAT 151, 161, 181, 235, 265, SCI 151, or MATH 181.
  • Credit exclusions: Credit cannot be obtained in CMPUT 466 if credit has already been obtained for CMPUT 467.
  • Offered: Now: Fall 2026
  • Difficulty: Hard-Brutal (instructor-dependent)
  • Workload: Heavy

Learning is essential for many real-world tasks, including recognition, diagnosis, forecasting and data-mining. This course provides a broad overview of topics in machine learning, from foundational methods for regression, classification and dimensionality reduction to more complex modeling with neural networks. It will also provide the formal foundations for understanding when learning is possible and practical. This single course is an alternative to the more in-depth two-course sequence on machine learning with CMPUT 267 and CMPUT 467.

Official catalogue entry ยท facts synced 2026-10-01

What Students Say

Imported from the original course reviews page, which mixed student contributions with summarized Reddit discussion. Tips aren't term-stamped yet; if you've taken this course recently, add your experience.

Supervised learning, neural networks, probabilistic models, optimization. The experience varies a lot by section, so it's worth asking upper-years about the current instructor.

Instructor notes: - Dale Schuurmans: students praise the textbook he uses and describe exams and assignments as fair. - Russ Greiner: enthusiastic lecturer; former students credit his section with launching their interest in ML.

Student tips: - Check who is teaching before registering and ask upper-years how that section has been run (exam format, pacing, practice material). - CMPUT 267 and MATH 125 are important background. Students who skipped 267 often feel lost on the math. - 466 is the one-course alternative to the 267 + 467 sequence. If you're in an AI Option or want deeper ML, check whether your program needs 467 instead; you can't get credit for 466 once you have 467.