Large Language Models: Foundations, Training, and Inference
Introduction to the foundations and applications of large language models. Topics include tokenization, embeddings, attention, transformers, pretraining, fine-tuning, prompting, inference strategies, evaluation, and responsible use of LLMs. Students gain hands-on experience implementing and analyzing language models through coding assignments and a course project.
Prerequisites & course information
Prerequisites: STAT 362-0 , COMP_SCI 349-0 , or STAT 415-0 ; and MATH 240-0 , MATH 281-3 , MATH 285-1 , MATH 290-1 , MATH 291-1 , GEN_ENG 205-1 , or GEN_ENG 206-1 ; and STAT 320-1 , STAT 383-0 , MATH 310-1 , MATH 311-1 , MATH 314-0 , MATH 385-0 , ELEC_ENG 302-0 , IEMS 302-0 , or equivalent. Programming experience in Python is expected. Students with comparable preparation may enroll with permission of the instructor. SoC Individual and Social Behavior Distro SoC Mathematics, Science, and Technology Distro
Sources
- Course catalogue ↗2026–2027
- Catalogue entry ↗
Catalogue variants
- Large Language Models: Foundations, Training, and Inference · 1 Unit · Undergraduate