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CS 479Neural Networks

Units
0.50
Faculty
Mathematics
Subject
Computer Science
Cross-listed
AMATH 449

When it runs

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

Ran in 6 of the last 7 terms. Winter 2027: scheduled, 4 lecture sections. Spring 2027: schedule not out yet.

An introduction to neural network methods, with some discussion of their relation to neuroscience. Simple neuron models and networks of neurons. Training feedforward networks. Backpropagation of errors and stochastic gradient descent. Unsupervised learning methods. Recurrent neural networks. Convolutional neural networks. Continuous time models. Adversarial attacks. Advanced topics may include neural engineering, biologically plausible learning methods.

Prerequisites

  • Complete 1 of the following
    • Must have completed at least 1 of the following:
      • STAT230 - Probability (0.50)
      • STAT240 - Probability (Advanced Level) (0.50)
    • Earned a minimum grade of 60% in each of the following:
      • STAT206 - Statistics for Software Engineering (0.50)

Corequisites

  • Completed or concurrently enrolled in at least 1 of the following:
    • AMATH242 - Introduction to Computational Mathematics (0.50)
    • CS335 - Computational Methods in Business and Finance (0.50)
    • CS370 - Numerical Computation (0.50)
    • CS371 - Introduction to Computational Mathematics (0.50)

Antirequisites

None listed.

Leads to

No course lists it as a requisite.

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 001676361 / 60MW 14:30–15:50
LEC 002676456 / 60MW 16:00–17:20
TST 1016771117 / 120Oct 22 08:30–10:20

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 00160730 / 70MW 10:00–11:20
LEC 00262370 / 70MW 14:30–15:50
LEC 00385030 / 70MW 16:00–17:20
LEC 7012373207 / 9999Online or by arrangement
TST 10162980 / 210Feb 23 19:00–20:50

Reviewed on UW Flow

83% liked it63% easy77% useful

From 23 ratings and 11 comments on UW Flow. The most helpful comments, quoted as written, without names. Read them all on UW Flow.

  • Took in F25 with Prof. Mohamed Hibat-Allah. Chill course on neural networks and deep learning including NNs, CNNs, RNNs, diffusion models, transformers/attention, and GANs. Course content was interesting but exams and assignments were too easy in that you don't actually need to understand the content to do well on them. For example, mathematical proofs on assignments were either in the notes or could be done just with basic calc and lin alg, without an intuition for the actual material. So if you take this course, make sure to actually understand the material to get the most out of it. A lot of breadth but not too much depth into each topic, but overall a great and fun course.

    May 2026 · liked itOn UW FlowRemove this

  • Interesting and useful course that provides an overview of the major deep learning architectures but can get pretty in the weeds with the calc and lin alg. Midterm and final are both fair, with the weekly assignments well-designed to reinforce key concepts. The topics in the last 1/3 could do with more polish, but overall the course does a solid job illustrating neural networks and their evolution.

    Apr 2026 · liked itOn UW FlowRemove this

  • First half was super interesting and relatively easy. From the second half the course ramps up. Felt like the course covered a lot of breadth. Midterm was fair. Final was a lil tricky. Definitely need to brush up on calculus and chain rule before taking this course. Assignments were not as bad. Doable in a day. Jeff is an amazing prof tho so if you get the chance take it with him.

    Apr 2026 · liked itOn UW FlowRemove this

  • Assignments have coding questions that are worth anywhere from 1/2-3/4 of the total marks, they get annoying at times but some of them are pretty cool. Midterm wasn't too difficult. This course is basically a combination of every math course you've ever taken - graph theory, linalg, calculus, stats, encodings, adversarial attacks... so you really gotta know your first and second year math stuff well.

    Apr 2026 · liked itOn UW FlowRemove this

  • Super interesting course if you want to learn more about ML and AI. Near the end of the course, we learned about the high-level architecture of how LLMs like ChatGPT work, and this literally triggered an existential crisis after seeing the complete breakdown of earlier GPT models. Some concepts in the course were pretty conceptually difficult, such as RBMs and Autoencoders. A lot of content is covered; don't let the first few lectures fool you into thinking this is a neurobiology course. Make sure you have a VERY good understanding of probability laws (expected value, conditional, joint, law of total probability, etc), Matrix algebra, and especially chain rule. Pretty much everything in…

    Dec 2025 · liked itOn UW FlowRemove this

Discussed on r/uwaterloo

47 posts and comments in 42 threads mention CS 479, May 2020 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.

Fall 2024

Easiest Faculty of Mathematics Course for Engineering AI option 5 points, 7 comments

u/Agishan: “… (0.50) CO466 \- Continuous Optimization (0.50) CS452 \- Real-Time Programming (0.50) CS479 \- Neural Networks (0.50) CS484 \- Computational Vision (0.50) STAT341 \- Computational Statistics and Data Analysis (0.50) STAT440 \- Computational Inference (0.50) STAT441 \- Statistical …”

u/OkRevolution1962: “341 is pretty easy but boring and annoying as hell. Pretty sure CS 479 isn’t hard and a little more interesting and applicable. CO 456 I’ve heard is also really fun and not too bad if you’re decent at math”

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Most interesting upper-year CS classes 13 points, 8 comments

“Rising 4B CS here. Currently on my schedule I have 488 (Graphics) since I’ve been meaning to take that since like 2nd year and CS 492 since I feel like it’d be fun to talk about the social …”

u/COCS2022: “CS 479 (neural networks) is relatively interesting and easy. it's a light version of CS 480.”

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Spring 2024

Winter 2024

CS course recommendation 1 point, 6 comments

“Hey y'all. I'm a graduate student at the school pharmacy. I plan to incorporate AI in drug discovery and I wonder if the CS department has any courses that can help me get started with AI. …”

u/Osteospermum: “… The undergrad courses I think would be most relevant to you are CS 479 to learn about neural networks and modern ML paradigms and CS 480 which is general machine learning and will provide a strong foundation. …”

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

“After going through UWFlow, I found a shocking amount of Intro ML/AI courses like: CS 480: Introduction to Machine Learning CS 485: Statistical and Computational Foundations of Machine Learning CS …”

u/Osteospermum: “If you just want to be prepared for industry, like for an MLE role, I’d say CS 479 and CS 480. I can’t speak to MSCI and ECE courses but CS 485 is much more theoretical and less practical and CS 486 focuses more on artificially intelligent agents (e.g. …”

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Fall 2023

Check it elsewhere for Fall 2026

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