IE 7295 — Applied Reinforcement Learning in Engineering
4 semester hoursGraduateLectureusually offered: fall, springBostonOnlineTraditionalVideo Streaming
Covers fundamentals of reinforcement learning (RL) and its applications in engineering areas. Provides an overview of the RL concepts and important algorithms. Demonstrates applications of RL to address engineering problems in manufacturing, supply chain, healthcare, and engineering economics. Offers students an opportunity to master their skills to apply RL to practical engineering projects through a series of lab sessions.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2025 | 1 | 1 | 20 | 5% | 19.0 |
| Fall 2025 | 1 | 3 | 10 | 30% | 7.0 |
| Spring 2026 | 1 | 13 | 20 | 65% | 7.0 |
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 18% · T 0% · W 18% · Th 0% · F 0%
Common patterns: async (82% of sections), MW (18% of sections) — in patterns, R means Thursday
Professors
Fall
- Mohammad Mohammad Dehghani Dehghani (100% of students) · reviews
Spring
- Mohammad Mohammad Dehghani Dehghani (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (IE course descriptions) · All IE courses · Plan it at numap.app