COE4216935 - Introduction to Large Language Models - Spring 2026

Course Info:

This course gives a full introduction to Large Language Models (LLMs). It covers the whole process, from basic ideas to real applications and deployment. Students will learn how transformer models work, how pre-training and fine-tuning are done, and how to build and deploy applications using LLMs. The course mixes theory with practical exercises, so students can get both understanding and hands-on skills. By the end, students will be prepared to work with and understand the fast-growing field of generative AI.

Prerequisites

Intermediate Python programming
Linear algebra, probability, and optimization
Previous coursework in machine learning

Required Text(s)

Speech and Language Processing (3rd ed. draft) Dan Jurafsky and James H. Martin (html).

Recomended Text(s) and Resources

- Hands-On Large Language Models by Jay Alammar, Maarten Grootendorst (2024)
( https://github.com/HandsOnLLM/Hands-On-Large-Language-Models )

- LLM Engineer�s Handbook by Paul Iusztin, Maxime Labonne (2024)
( https://github.com/PacktPublishing/LLM-Engineers-Handbook )
- Build a Large Language Model (From Scratch)
( https://github.com/rasbt/LLMs-from-scratch )
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (https://github.com/ageron/handson-ml3)
- Deep Learning for Coders with fastai and PyTorch (https://github.com/fastai/fastbook)
- HuggingFace NLP Course (html)
- Practical Natural Language Processing (2020) (https://github.com/practical-nlp)
- Natural Language Processing with Transformers, Revised Edition (2022) (https://github.com/nlp-with-transformers )
- Natural Language Processing with PyTorch (2019) (https://github.com/delip/PyTorchNLPBook)
- How To Scale Your Model ( https://jax-ml.github.io/scaling-book/ )
- https://github.com/mlabonne/llm-course
- https://github.com/SylphAI-Inc/LLM-engineer-handbook
- https://apxml.com/courses/intro-large-language-models
- https://apxml.com/courses/how-to-build-a-large-language-model
- https://apxml.com/courses?category=Large+Language+Models

Meeting Times:

- Wednesday 10:00 - 13:00, Location: C-314

Labs:

- Lab : No labs

Tentative Grading:

Evaluation Tool (*)	Weight in %
Assignments, Presentations and Projects	30
In-term Exams - 1 Midterm	30
Final	40

(*)

Tentative Course Outline:

WEEK	TOPIC(S)
1	Introduction
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Course Syllabus in PDF ()