Large Language Models
COMP 4901B, Fall 2025
The Hong Kong University of Science and Technology
Junxian He
Course description: this is an undergraduate-level course to cover the basics and latest advances about large language models. This course will start with the basics of language models, and then cover the latest advances in large language models. It will include core techniques on developing LLMs such as DeepSeek-R1, Qwen, LLama, etc. Applications and potential risks of LLMs will also be covered. Throughout this course, you are expected to (1) know the basic principles of language models and how it works; (2) be able to use LLMs and know how to tame it in practice; (3) understand the core development techniques and are able to train language models in relatively simple scenarios.
Course Information
Course Logistics (Grading, Policy, etc.)
Time and Location
Lectures: Wed, Fri 4:30 PM - 5:50 PM at Multi-function Room, LG4, LIB
Course Staff and Office Hours
Instructor: Junxian He. Office Hour: Wed 10 AM - 11 AM at CYT3004
TA1: Yuzhen Huang. Office Hour: Wed 10AM - 11AM at Zoom
TA2: Wei Liu. Office Hour: Tue 2PM - 3PM at Zoom
Contact and Communication
Canvas is the main platform for communication about technical questions of lecture contents and homeworks. Please do not directly email the teaching staff on technical questions which may easily get lost. The teaching staff will try to answer questions on Canvas in a prompt manner. Some rules:
- All announcements and communications will happen over Canvas
- Please utilize the Canvas Discussion page to ask questions. There are pre-existed discussion topics initiated by the staff such as lecture questions and each homework. Please post your questions in the respective thread, you are also encouraged to answer others’ questions if you know the answer.
- While we allow students to optionally post anonymous questions and answers on Canvas, please do not post anything inappropriate as anonymous users – in such cases, we will turn off the anonymous option.
- If you have non-technical, private matters, please email the teaching staff directly.
- Please consult the Course Logistics page before asking logistical questions.
- For longer discussions, please come to office hours.
Prerequisites and Materials
Students are required to have taken machine learning courses and know basics about machine learning and deep learning.
This course does not have a textbook as large language models is a fast-evolving field, but I will recommend blogs or papers to read for the respective lecture.
Lecture Schedule
The lecture schedule below is tentative and subject to change.
| Slides | Date | Topic | Readings | Assignments |
|---|---|---|---|---|
| Lecture 0 | 03/09 Wed | Introduction to Large Language Models | ||
| Lecture 1 | 05/09 Fri | Machine Learning Basics | ||
| Lecture 2 | 10/09 Wed | Language Model | ||
| Lecture 3 | 12/09 Fri | Neural Networks and Transformers | ||
| Lecture 4 | 17/09 Wed | Transformers | ||
| Lecture 5 | 19/09 Fri | Pretraining: Objectives and Data Curation | ||
| Lecture 6 | 24/09 Wed | In-Context Learning, Prompt Engineering | ||
| Lecture 7 | 26/09 Fri | Evaluation of Large Language Models | ||
| 01/10 Wed | The National Day Holiday | |||
| Lecture 8 | 03/10 Fri | Instruction Tuning and Alignment | ||
| Lecture 9 | 08/10 Wed | Instruction Tuning and Alignment | ||
| Lecture 10 | 10/10 Fri | Reinforcement Learning Basics | ||
| Lecture 11 | 15/10 Wed | Reinforcement Learning from Human Feedback (RLHF) | ||
| Lecture 12 | 17/10 Fri | Scaling Up LLMs: Scaling Laws | ||
| Lecture 13 | 22/10 Wed | Chain-of-Thought Reasoning | ||
| Lecture 14 | 24/10 Fri | Deep Reasoning Models | ||
| 29/10 Wed | Chung Yeung Festival | |||
| Lecture 15 | 31/10 Fri | Synthetic Data Training: Data Synthesis and Distillation | ||
| Lecture 16 | 05/11 Wed | Synthetic Data Training: Self-Improving through RLVR | ||
| Lecture 17 | 07/11 Fri | LLM Agents | ||
| Lecture 18 | 12/11 Wed | LLM Agents | ||
| Lecture 19 | 14/11 Fri | LLM Safety: Attacks and Defense | ||
| Lecture 20 | 19/11 Wed | Hallucination and RAG | ||
| Lecture 21 | 21/11 Fri | Bias and Ethics | ||
| Lecture 22 | 26/11 Wed | Sparse Models | ||
| Lecture 23 | 28/11 Fri | Review Representative LLMs |