EECE 5668 — Large Language Models
4 semester hoursGraduateLectureusually offered: springtypical days: ThBostonTraditional
Explores the foundations, methodologies, and applications of large language models (LLMs), cutting-edge systems reshaping the field of artificial intelligence. Covers core topics including model architectures, training objectives, fine-tuning strategies, prompt engineering, evaluation metrics, and challenges such as bias, fairness, and interpretability. Highlights real-world uses of LLMs in tasks such as text generation, translation, summarization, and reasoning. Offers opportunities to examine recent research and trends driving the rapid evolution of LLMs. Emphasizes both theoretical understanding and practical applications using widely adopted tools and libraries for developing and deploying LLMs. Encourages critical thinking about the societal implications of deploying these models at scale.
Prerequisites
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2026 | 1 | 11 | 60 | 18% | 49.0 |
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 100% · F 0%
Common patterns: R (100% of sections) — in patterns, R means Thursday
Professors
Spring
- Roi Yehoshua (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (EECE course descriptions) · All EECE courses · Plan it at numap.app