University of Toronto Mississauga
CSC311H5
Introduction to Machine Learning
What was the average?
Not reported
No student has contributed a result for this offering yet.
How is it graded?
30% final
From the 2025 syllabus. 8 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 methods for automated learning of relationships on the basis of empirical data. Classification and regression using nearest neighbour methods, decision trees, linear models, and neural networks. Clustering algorithms. Problems of overfitting and of assessing accuracy.
- Exclusions
- CSC311H1CSC411H5CSCC11H3
- Contact hours
- 24 lecture · 12 practical
- Total scheduled hours for the course, not per week.
Recommended preparation — not required
The calendar advises these before taking CSC311H5. You can enrol without them.
Where the marks come from
2025 syllabusExtracted from a published 2025 syllabus by deterministic parser. Components that could not be labelled are shown unresolved rather than folded into a neighbouring category.
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 |
| Assessment weighting | Course syllabus, 2025 | 2026-07-25 | summary permitted |
Page last verified 2026-07-25. A claim whose source moves is withdrawn before it goes stale.
What this opens up
8 courses list CSC311H5 as a prerequisiteThe other 4 have no page yet. Listing a course here means it names CSC311H5 among its prerequisites — often as one of several ways to satisfy the same requirement, so this is what CSC311H5 makes available, not what it alone guarantees.
Around this course
Everything above is history. Yours isn’t written yet.
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