DSCI325
APPLIED MACHINE LEARNING
- Department
- Data Science
- Terms offered
- 3
- Data refreshed
- Jun 27, 2026
Description
This course will be an investigation into the fundamental techniques and practices of machine learning (ML). The primary activities of the course will be a series of lectures and a corresponding series of lab projects, written reflections, a final project and paper. The course will focus on applying ML to practical problems in Data Science and will also introduce the student to some of the fundamental theory of ML.
Syllabi
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Offering history & forecast 2024–2026
2024 2026
| Year | Fall | Spring | Summer | Intersession |
|---|---|---|---|---|
| 2030 | 2030 Fall: 7% chance, medium confidence. Offered in 0 of 9 fall terms since 2018. | 2030 Spring: 60% chance, low confidence. Offered in every spring term since 2024. Department course count rising about 16% per year. Department faculty is about 55% above its recent average. | 2030 Summer: 7% chance, medium confidence. Offered in 0 of 9 summer terms since 2018. | 2030 Intersession: 8% chance, medium confidence. Offered in 0 of 6 intersession terms since 2020. |
| 2029 | 2029 Fall: 6% chance, medium confidence. Offered in 0 of 10 fall terms since 2017. | 2029 Spring: 64% chance, low confidence. Offered in every spring term since 2024. Department course count rising about 22% per year. Department faculty is about 69% above its recent average. | 2029 Summer: 6% chance, medium confidence. Offered in 0 of 10 summer terms since 2017. | 2029 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2028 | 2028 Fall: 5% chance, medium confidence. Offered in 0 of 11 fall terms since 2016. | 2028 Spring: 67% chance, low confidence. Offered in every spring term since 2024. Department course count rising about 21% per year. Department faculty is about 76% above its recent average. | 2028 Summer: 5% chance, medium confidence. Offered in 0 of 11 summer terms since 2016. | 2028 Intersession: 7% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2027 | 2027 Fall: 5% chance, high confidence. Offered in 0 of 12 fall terms since 2015. | 2027 Spring: 71% chance, low confidence. Offered in every spring term since 2024. Department course count rising about 21% per year. Department faculty is about 76% above its recent average. | 2027 Summer: 5% chance, high confidence. Offered in 0 of 12 summer terms since 2015. | 2027 Intersession: 6% chance, medium confidence. Offered in 0 of 7 intersession terms since 2017. |
| 2026 | Not offered | 1 section | Not offered | No data for this term |
| 2025 | Not offered | 1 section | Not offered | Not offered |
| 2024 | Not offered | 1 section | Not offered | Not offered |
Forecast Under 15% 15–39% 40–69% 70–89% 90%+ Not enough history
Fall Spring Summer Intersession Darker means more sections Not offered No data for term
Future estimates combine recent offering frequency with detected multi-year cycles. Hover a cell for confidence and evidence; low-confidence estimates are marked with ~.
Meeting times
Most recent first- Mon, Fri, 9:00 AM–10:15 AM MACD117 2026-12025-1
- Wed, 9:00 AM–10:15 AM MACD117 2026-22025-2
| Term | Professor | Type | Credits | Enrollment | CRN |
|---|---|---|---|---|---|
| Spring 2026 | Catalin C. Veghes | Lecture | 1 | 1 of 30 enrolled | 33488 |
| Spring 2025 | Catalin C. Veghes | Lecture | 1 | 0 of 30 enrolled | 33488 |
| Spring 2024 | Catalin C. Veghes | Lecture | 1 | 0 of 24 enrolled | 33488 |