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CMPUT 267: Machine Learning I

At a glance

  • Units: 3
  • Prerequisites: CMPUT 174 or 274, or ENCMP 100; one of MATH 100, 114, 117, 134, 144, or 154.
  • Corequisites: CMPUT 175 or 275; CMPUT 272; MATH 102, 125, 126, or 127; one of STAT 151, 161, 181, 235, 265, SCI 151, or MATH 181.
  • Offered: Now: Fall 2026; Scheduled: Winter 2027
  • Difficulty: Hard
  • Workload: Heavy
  • Languages and tools: Python, Google Colab

This course introduces the fundamental statistical, mathematical, and computational concepts in analyzing data. The goal for this introductory course is to provide a solid foundation in the mathematics of machine learning, in preparation for more advanced machine learning concepts. The course focuses on univariate models, to simplify some of the mathematics and emphasize some of the underlying concepts in machine learning, including: how should one think about data, how can data be summarized, how models can be estimated from data, what sound estimation principles look like, how generalization is achieved, and how to evaluate the performance of learned models.

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.

Statistics-heavy intro to ML: linear algebra, probability, gradient descent, regression, classification. This is not a "here's how to use scikit-learn" course. It's mathematical foundations. Coding is in Python on Google Colab (per the Fall 2025 course site, which had 8 assignments with at least 3 involving code, plus two midterms and a final).

One student summarized it well: "It's basically applied stats. That's what a lot of ML is." Another: "My roommate took 267 and it was TOUGH."

Student tips: - Strong stats and linear algebra background (MATH 125, STAT 151/252) helps a lot. - Don't take this with 379 or other heavy courses. One student who took 379, 401, and 267 in the same semester was specifically advised to drop 267. - 267 is one of the ways into 365 (Introduction to Reinforcement Learning), which requires one of 267, 466, or STAT 265.