PHYS 4211 — Artificial Intelligence for Physical Science

4 semester hoursUndergraduateLectureusually offered: springtypical days: FBostonTraditional

Introduces artificial intelligence (AI) and machine learning (ML) approaches that are powering data-driven scientific research. Gives students a broad overview of why and how ML is used in multiple science disciplines. Introduces the underlying principles of a diverse set of ML models and AI frameworks. Discusses how to combine domain knowledge with AI tools to advance research and solve challenging problems in multiple fields of physical science.

Prerequisites

all of:
- one of:
  - EECE 2140 (min D+)
  - PHYS 1211 (min D+)
  - PHYS 3211 (min D+) (may be taken concurrently)
- PHYS 2303 (min D+) (may be taken concurrently)

Offering history

Term	Sections	Enrolled	Capacity	Full	Open seats/section
Spring 2026	1	5	48	10%	43.0

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 100%

Common patterns: F (100% of sections) — in patterns, R means Thursday

Professors

Spring

Qimin Yan (100% of students) · reviews

Percentages are each professor's average share of the season's enrolled students in recent terms.

Links

Official catalog (PHYS course descriptions) · All PHYS courses · Plan it at numap.app