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MSE 446Introduction to Machine Learning

Units
0.50
Faculty
Engineering
Subject
Management Science and Engineering

When it runs

  1. F24
  2. W25
  3. S25
  4. F25
  5. W26
  6. S26
  7. F26
  8. W27
  9. S27

Ran in 7 of the last 7 terms. Winter 2027: scheduled, 2 lecture sections. Spring 2027: schedule not out yet.

This course provides an introduction to machine learning, including supervised and unsupervised learning. Emphasis is placed on proper procedures for the training and testing of models. Topics covered may include data cleaning and transformation, overfitting and generalization, n-fold cross validation, regression, decision trees, neural networks, rule finding, and clustering. Students learn to apply machine learning methods to management engineering problems using common tools such as R and Python.

Prerequisites

  • Complete all of the following
    • Complete 1 of the following
      • Must have completed the following:
        • CS231 - Algorithmic Problem Solving (0.50)
        • CS234 - Data Types and Structures (0.50)
      • Must have completed at least 1 of the following:
        • BME122 - Data Structures and Algorithms (0.50)
        • CS240 - Data Structures and Data Management (0.50)
        • CS240E - Data Structures and Data Management (Enriched) (0.50)
        • ECE250 - Algorithms and Data Structures (0.50)
        • MSE240 - Algorithms and Data Structures (0.50)
        • MTE140 - Algorithms and Data Structures (0.50)
        • SYDE223 - Data Structures and Algorithms (0.50)
      • Must have completed the following:
    • Complete 1 of the following
      • Must have completed at least 1 of the following:
        • AE224 - Probability and Statistics (0.50)
        • BME213 - Statistics and Experimental Design (0.50)
        • CHE220 - Probability and Statistics (0.50)
        • CIVE224 - Probability and Statistics (0.50)
        • ECE307 - Probability Theory and Statistics 2 (0.50)
        • ENVE224 - Probability and Statistics (0.50)
        • ME202 - Statistics for Engineers (0.50)
        • MSE253 - Probability and Statistics 2 (0.50)
        • MTE201 - Experimental Measurement and Statistical Analysis (0.50)
        • NE215 - Probability and Statistics (0.50)
        • STAT206 - Statistics for Software Engineering (0.50)
        • STAT231 - Statistics (0.50)
        • STAT241 - Statistics (Advanced Level) (0.50)
        • SYDE212 - Probability, Statistics, and Data Science (0.50)
      • Must have completed the following:
    • Students must be in level 3A or higher

Antirequisites

  • Not completed nor concurrently enrolled in any of the following:
    • BME522 - Foundations of Artificial Intelligence (0.50)
    • CHE523 - Introduction to Machine Learning and Data Science (0.50)
    • CS480 - Introduction to Machine Learning (0.50)
    • ECE457B - Machine Learning (0.50)
    • SYDE522 - Foundations of Artificial Intelligence (0.50)

Leads to

Fall 2026 sections

Enrolment from UW Open Data, which can trail Quest by up to two days. Instructors and rooms: Schedule of Classes.
SectionClassEnrolledMeets
LEC 001537243 / 64Th 18:30–21:20
TUT 101537343 / 64T 17:30–18:20

Winter 2027 sections

Enrolment from UW Open Data, which can trail Quest by up to two days. Instructors and rooms: Schedule of Classes.
SectionClassEnrolledMeets
LEC 00150600 / 202TTh 11:30–12:50
LEC 7012045150 / 9999Online or by arrangement
TUT 10150620 / 202W 08:30–09:20

Reviewed on UW Flow

57% liked it73% easy60% useful

From 42 ratings and 15 comments on UW Flow. The most helpful comments, quoted as written, without names. Read them all on UW Flow.

  • After learning about the course from uppers and how useful it is, I went into this class with high hopes due to the high praise to Lukas Golabz. However the Spring 2024 course was taught by Merhdad who intentionally made it more difficult. The content as mostly statistics instead of focusing on concepts and why they were important to ML. This change in teaching method, made a lot of the people interested in ML, turned away. The midterm average of this course was high 40s, which was after he handed us the main topics of the exam. For an intro course this was just not it. Before selecting this course check the prof, if its Merdad choose something else!

    Apr 2024 · didn’t like it · 3 found this helpfulOn UW FlowRemove this

  • Previously, only good words were spoken about this course but that all changed with the new professor for W24. He was upset about class attendance even though he would just read off slides/modules for the duration of the lecture. More than machine learning this felt like a high level statistics course that took away the essence of the machine learning aspect of it. Not only that but the course had 5 TA’s who were basically only there to mark the project and not really useful otherwise. And the professor did his due diligence to make the most difficult midterm he possibly could (Class average around 50 (probably less)). This class single handedly made this term unbearable.

    Apr 2024 · didn’t like it · 2 found this helpfulOn UW FlowRemove this

  • The actual content of the course was pretty interesting, however, the professor would make slide shows up to 120 slides long. It could have been a really cool course, but the execution was really poor.

    Sep 2026 · didn’t like itOn UW FlowRemove this

  • I did not like the professors teaching methods

    Sep 2026 · didn’t like itOn UW FlowRemove this

  • Overall, the course was chill, we had 4 term tests and a final project. However, I expected more going into this course. Instead, it was a very shallow overview of basic concepts with little math to back it up (which was shocking for a 4th year technical elective). Additionally, although the concepts taught were simple, the marking is very keyword focused. So I recommend word vomiting to decrease your odds of getting points deducted. Also, the prof didn’t provide practice problems :(

    Aug 2026 · didn’t like itOn UW FlowRemove this

Discussed on r/uwaterloo

4 posts and comments in 3 threads mention MSE 446, April 2024 to December 2024. Quoted word for word, newest first; each reply sits under the post and the comment that started its branch. Search r/uwaterloo for newer posts.

Spring 2024

CE 3B Elective Advice 3 points, 3 comments

u/ivythe1st: “… I was thinking of doing another TE as well so I can free up space in 4A and 4B: I'm really interested in SYDE 522 (Foundations of AI) or MSE 446 (Intro to ML). …”

u/Swaggy_Baller: “Are you taking MSE 446? If so, how did you satisfy the ECE 307 prereq?”

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Course Selection for AI/ML + imaging 7 points, 6 comments

u/jawyau: “… ECE 457A ECE 457B SYDE 522 MSCI/MSE 446 SYDE 575 SYDE 572 ECE 495 CS 488 CS 484 I ideally want a lighter term but I also understand that some of the good courses are difficult so I'm willing to put in the effort if it's really worth it in the long run. …”

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Winter 2024

Questions for Management Engineering students: this program or UTSG CS? 1 point, 11 comments

u/KINGBLUE2739046: “- It’s an Engineering workload, it’s a lot better than ECE, much more time to do side projects, almost a joke honestly, other programs will be making fun of you for not being real engineers, no big …”

u/nyephe: “… They changed MSE 121/240 but I've never used Python in MGTE before (aside from MSE 446) R is pretty much just copy and paste from the MSE 251/253 tutorials but it's never tested I remember Excel being used in MSE 100/131/251/253/331/263/334/431/332/333/343, and this is only up …”

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Check it elsewhere for Fall 2026

Calendar versions

Undergraduate Calendar