uwloo

ECE 457BMachine Learning

Units
0.50
Faculty
Engineering
Subject
Electrical and Computer Engineering

When it runs

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

Ran in 5 of the last 7 terms. Winter 2027: not on the schedule. Spring 2027: schedule not out yet.

Fundamentals and recent advances in computational intelligence. Building accurate models with collected data or rules bases. Model-based prediction and classification. Concepts in machine learning, supervised and unsupervised learning, artificial neural networks, deep learning, feature extraction, feature selection, dimensionality reduction, classification and clustering, support vector machines. Approximate reasoning based on fuzzy set theory. Performance metrics to assess the validity of produced models. Multiple examples and case studies such as autonomous driving, intelligent manufacturing, natural language understanding, speech recognition, computer vision, stock market prediction, disease early detection and diagnosis.

Prerequisites

  • Complete all of the following
    • Complete 1 of the following
      • Must have completed at least 1 of the following:
        • BME122 - Data Structures and Algorithms (0.50)
        • CS240 - Data Structures and Data Management (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:
    • Students must be in level 4A or higher
    • Complete 1 of the following

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)
    • CS486 - Introduction to Artificial Intelligence (0.50)
    • MSE446 - Introduction to 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 001544890 / 96M 14:30–16:20
W 14:30–15:20
TUT 101544990 / 96W 13:30–14:20

Resources

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Reviewed on UW Flow

54% liked it39% easy84% useful

From 24 ratings and 13 comments on UW Flow. The most helpful comments, quoted as written, without names. Read them all on UW Flow.

  • If your goal is to learn more about machine learning, don't take this course. Take Autonomous driving ECE 493 instead.

    Apr 2018 · didn’t like it · 1 found this helpfulOn UW FlowRemove this

  • Took it with Prof. Amir-Hossein Karimi
    As someone who showed up to every class, I would not recommend it as it would be a waste of time when you understand what the final exam is like.
    Course content was very high level and the assignments had interesting applications. But everyone was just so blindsided by the difficulty of his exam and how disparate the lectures are to the questions you were asked.

    Sep 2026 · didn’t like itOn UW FlowRemove this

  • Took it with Prof. Amir-Hossein Karimi. Content is interesting, there are a lot to digest but I think this is due to the nature of the topic itself. It's worth the effort to read through every slide carefully.

    May 2024 · liked itOn UW FlowRemove this

  • Keywords: 1. Machine Learning newbie friendly 2. Average course load 3. Not likely to get extremely high or low marks.
    As expected for an "Introductory" course in Computational intelligence, this course covers many ML topics (but not that deep). Recommended for those who wanna know about ML and only have a little prior knowledge. (Took ECE495 this term as well. That one covers far more topics. Recommend ECE495 if you already know the basics of ML and start to challenge yourself with ML papers) Lectures will focus on topics whereas tutorials will focus on ML projects using Google Colab.
    It is hard to get extremely high or low marks. Machine Learning is an Engineering topic where there's…

    Apr 2023 · liked itOn UW FlowRemove this

  • Professor Mehrannia focused on shifting the course's primary focus from fuzzy logic, which was less valuable, to modern ML and DL, which were more applicable. I gained fundamental insights into ML and DL and learnt something useful from the course.

    Mar 2023 · liked itOn UW FlowRemove this

Discussed on r/uwaterloo

34 posts and comments in 32 threads mention ECE 457B, August 2012 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

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

Best Intro to ML/AI course? 15 points, 14 comments

u/bomankleinn01: “… CS 486: Introduction to Artificial Intelligence MSCI 452: Introduction to Machine Learning ECE 457B: Fundamentals of Computational Intelligence ECE 457: Applied Artificial Intelligence &x200B; What's the best for industry Intro ML course from this list and the difference between …”

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Fall 2023

Spring 2022

A GUIDE ON HOW TO PLAN AN OPTION 29 points, 15 comments

u/1000Ditto: “Hey all, here's an example (by yours truly) of how to plan an option. as someone who has been trying to get many options in but having a hard time, might as well write down my process so others can kind of benefit from it. …”

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Winter 2022

Winter 2020

Winter 2019

Check it elsewhere for Fall 2026

Calendar versions

Undergraduate Calendar