DSCI225
APPLIED MACHINE LEARNING
- Department
- Data Science
- Terms offered
- 6
- Data refreshed
- Jun 27, 2026
Description
An investigation into the fundamental techniques and practices of machine learning. The primary activities of the course will be a series of lectures and a corresponding series of lab sessions, programming projects, and written assignments. The course will focus on applying machine learning to practical problems in Data Science and will also introduce the student to some of the fundamental theory of machine learning.Prerequisites for this course require a grade of C- or better.
Syllabi
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Offerings and forecast 2020–2026
2020 2026
| Year | Fall | Spring | Summer | Intersession |
|---|---|---|---|---|
| 2030 | 2030 Fall: 7% chance, medium confidence. Offered in 0 of 9 fall terms since 2018. | 2030 Spring: 66% chance, medium confidence. Offered in 6 of 7 spring terms since 2020. 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: 69% chance, medium confidence. Offered in 6 of 7 spring terms since 2020. 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: 72% chance, medium confidence. Offered in 6 of 7 spring terms since 2020. 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: 75% chance, medium confidence. Offered in 6 of 7 spring terms since 2020. 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 |
| 2025 | Not offered | 1 section | Not offered | Not offered |
| 2024 | Not offered | 1 section | Not offered | Not offered |
| 2023 | Not offered | 1 section | Not offered | Not offered |
| 2022 | Not offered | Not offered | Not offered | Not offered |
| 2021 | Not offered | 1 section | Not offered | Not offered |
| 2020 | 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 More sections Not offered No data
Meeting times
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
- Mon, Thu, 1:25 PM–2:40 PM BP326 2020-12020-2
- Tue, 1:25 PM–2:40 PM BP310 2020-3
| Term | Professor | Type | Credits | Enrollment | CRN |
|---|---|---|---|---|---|
| Spring 2026 | Catalin C. Veghes | Lecture | 1 | 30 of 30 enrolled | 32552 |
| Spring 2025 | Catalin C. Veghes | Lecture | 1 | 36 of 30 enrolled | 32552 |
| Spring 2024 | Catalin C. Veghes | Lecture | 1 | 15 of 24 enrolled | 32552 |
| Spring 2023 | Kenneth J. Basye | Lecture | 1 | 20 of 24 enrolled | 32552 |
| Spring 2021 | Kenneth J. Basye | Lecture | 1 | 22 of 24 enrolled | 37986 |
| Spring 2020 | Kenneth J. Basye | Lecture | 1 | 25 of 24 enrolled | 37986 |