Faculty of Arts & Science · St. George
STA414H1
Statistical Methods for Machine Learning II
What was the average?
C+
2.3 GPA across 1 observed term. Most recent: Winter 2026.
How is it graded?
Not yet verified
No syllabus we can lawfully use has been verified for this course.
How confident is this?
Low
A small number of terms, so read it as a range.
From the calendar
Official source · Faculty of Arts & ScienceProbabilistic foundations of supervised and unsupervised learning methods such as naive Bayes, mixture models, and logistic regression. Gradient-based fitting of composite models including neural nets. Exact inference, stochastic variational inference, and Marko chain Monte Carlo. Variational autoencoders and generative adversarial networks.
- Exclusions
- CSC412H1STAD68H3
- Contact hours
- 36 lecture
- Total scheduled hours for the course, not per week.
One option from each line
STA314H1, CSC311H1, CSC311H5, STA314H5 and STA315H5, or CSCC11H3
STA302H1, STAC67H3, or STA302H5
CSC108H1, CSC110Y1, CSC148H1, CSCA08H3, CSCA48H3, CSCA20H3, CSC108H5, or CSC148H5
MAT235H1 and MAT236H1, MAT235Y1, MAT237Y1, MAT257Y1, MATB41H3 and MATB42H3, MAT232H5 and MAT236H5, or MAT233H5 and MAT236H5
MAT223H1, MAT224H1, MAT240H1, MATA22H3, MATA23H3, MAT223H5, MAT240H5, MATB24H3, or MAT224H5
Recommended preparation — not required
The calendar advises these before taking STA414H1. You can enrol without them.
What people actually got
Last observed Winter 20261 term observed
This is a student-contributed aggregate, not an official University of Toronto record. It is also not a measure of how hard the course is — a course average moves with who enrols in it, not only with the material.
This rests on 1 reported term. It is shown because hiding it would not make it less true, but one or two results are a datapoint, not a distribution — a single unusual cohort moves this number a long way.
How well we know this course
2022–2024Pyko’s archive holds 3 documents for STA414H1, across 3 years between 2022 and 2024.
- 3
- Final exams
Counts, not a library. Pyko does not host or distribute course files, and this page is not an offer to supply one — the archive is what lets these pages describe how a course is assessed, which matters most where no syllabus has been verified.
What we don’t have yet
Source ledger
Report an error →| Claim | Source | Captured | Status |
|---|---|---|---|
| Course description, prerequisites, exclusions, contact hours | Faculty of Arts & Science | 2026-07-25 | public fact |
| Historical average across 1 term | Student contributions, consented | 2026-07-25 | user contributed aggregate |
| 3 archived documents, 2022-2024 | Pyko archive | 2026-07-25 | index only |
Page last verified 2026-07-25. A claim whose source moves is withdrawn before it goes stale.
Around this course
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