DA 5030 — Introduction to Data Mining/Machine Learning
4 semester hoursGraduateLectureusually offered: fall, springOnlineOnline
Introduces the fundamental techniques for data mining, combining elements from CS 6140 and CS 6220. Discusses several basic learning algorithms, such as regression and decision trees, along with popular data types, implementation and execution, and analysis of results. Lays the data analytics program foundation of how learning models from data work, both algorithmically and practically. The coding can be done in R, Matlab or Python. Students must demonstrate ability to set up data for learning, training, testing, and evaluating.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Fall 2023 | 2 | 57 | 108 | 53% | 25.5 |
| Spring 2024 | 1 | 42 | 59 | 71% | 17.0 |
| Fall 2024 | 1 | 51 | 59 | 86% | 8.0 |
| Spring 2025 | 1 | 46 | 79 | 58% | 33.0 |
| Fall 2025 | 1 | 45 | 50 | 90% | 5.0 |
| Spring 2026 | 1 | 54 | 59 | 92% | 5.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 0% · T 0% · W 0% · Th 0% · F 0%
Common patterns: async (100% of sections) — in patterns, R means Thursday
Professors
Fall
- Martin Schedlbauer (82% of students) · reviews
- Amin Assareh (18% of students) · reviews
Spring
- Martin Schedlbauer (70% of students) · reviews
- Amin Assareh (30% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (DA course descriptions) · Student reviews on RateMyHusky · All DA courses · Plan it at numap.app