AY2026/27 · Semester 1

Introduction to Generative AI with Foundation Models

CEG5305 · Department of Electrical and Computer Engineering, National University of Singapore · 4 MC, graduate level.

Lecturers · Liu Xingyu (Part 1) · Junyuan Hong (Part 2)

This site holds interactive companions to the lectures. Announcements, slides, homework, submissions and grades are on Canvas, which takes precedence wherever the two differ.

Part 2 lectures

Part 2 covers Topics 4–7, foundation model engineering. Each lecture is one syllabus topic; the six-hour topics run over two sessions.

Lecture 1 Architecture and training of LLMs Session 01 → Session 02 → Session 01: from n-grams to the Transformer, the components inside a GPT, ablation. Session 02: training, data, scaling laws, parameter-efficient fine-tuning.

Lecture 2Post-training and promptingcoming Two sessions.

Lecture 3Self-supervised foundation modelscoming One session.

Lecture 4Extending LLMs to large multi-modal modelscoming One session.

Course structure

Part	Topic	Hours
Part 1 Core generative modelling foundations	1 · Foundations and recap: CNNs, GANs → Transformers and language models	6
	2 · Autoregressive models and latent variable models	6
	3 · Diffusion models	6
Part 2 Foundation model engineering	4 · Architecture and training of LLMs	6
	5 · Post-training and prompting	6
	6 · Self-supervised foundation models	3
	7 · Extending LLMs to large multi-modal models	3

The schedule, assessment and reading list are published on Canvas.