Faculty of Arts & Science · St. George
ECO482H1
Machine Learning Applications in Macroeconomic Finance
What was the average?
Not reported
No student has contributed a result for this offering yet.
How is it graded?
30% final
From the 2024 syllabus. 5 graded components.
How confident is this?
Unknown
A small number of terms, so read it as a range.
From the calendar
Official source · Faculty of Arts & ScienceThis course introduces students to machine learning concepts and techniques, focusing on applications in macroeconomic finance. In the first part we review supervised and unsupervised machine-learning methods. In the second part, we focus on various questions and topics in macro-finance where the use of machine learning is increasingly common. Hands-on use of these techniques to solve economic problems in macro-finance will prepare students for more advanced machine learning courses.
- Exclusions
- CSC311H1ECO481H1STA314H1
- Contact hours
- 24 lecture · 12 tutorial
- Total scheduled hours for the course, not per week.
Where the marks come from
2024 syllabusExtracted from a published 2024 syllabus by deterministic parser. Components that could not be labelled are shown unresolved rather than folded into a neighbouring category.
How well we know this course
2023–2024Pyko’s archive holds 4 documents for ECO482H1, across 2 years between 2023 and 2024.
- 2
- Final exams
- 2
- Syllabi
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.
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 |
| Assessment weighting | Course syllabus, 2024 | 2026-07-25 | summary permitted |
| 4 archived documents, 2023-2024 | 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 A claim whose source moves is withdrawn before it goes stale.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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