University of Toronto Mississauga
CSC413H5
Neural Networks and Deep Learning
What was the average?
Not reported
No student has contributed a result for this offering yet.
How is it graded?
14 components
From the Fall 2026 syllabus. 14 graded components.
How confident is this?
Unknown
A small number of terms, so read it as a range.
From the calendar
Official source · University of Toronto MississaugaAn introduction to neural networks and deep learning covering backpropagation, architectures like convolutional and recurrent networks, optimization, generalization, and unsupervised and reinforcement learning.
- Prerequisite
- CSC311H5
- Exclusions
- CSC321H1CSC321H5CSC413H1CSC421H1
- Contact hours
- 24 lecture · 12 practical
- Total scheduled hours for the course, not per week.
Where the marks come from
Fall 2026 syllabusExtracted from a published Fall 2026 syllabus — verified against the university's own document. Components that could not be labelled are shown unresolved rather than folded into a neighbouring category.
- lowest 2 lab(s) dropped
What we don’t have yet
Source ledger
Report an error →| Claim | Source | Captured | Status |
|---|---|---|---|
| Course description, prerequisites, exclusions, contact hours | University of Toronto Mississauga | 2026-07-25 | public fact |
| Grading structure, description and credit weight from the official Fall 2026 syllabus (instructor: R. Hickey) | Official Fall 2026 course syllabus (University of Toronto Mississauga) | 2026-09-05 | official syllabus |
Page last verified 2026-09-05. A claim whose source moves is withdrawn before it goes stale.
Around this course
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