Large Language Models Course
Inha University Β· Spring 2026
This course covers large language models (LLMs) in a broad and systematic way. We will study the key topics needed to understand LLMs, including LLM architectures, training and inference, GPU systems, and RL-based post-training.
Note: The official course title at Inha University is Multimodal VLM (ECE7115), but the actual course content is centered on LLMs.
Instructor: Namhyuk Ahn (School of Electrical and Electronic Engineering, Inha University)
Schedule & Lecture Materials
Note: We provide lecture videos in Korean only.
| λ μ§ | λ΄μ© | Slides | YouTube |
|---|---|---|---|
| 3/2 | No class (National holiday) | ||
| 3/9 | No class | ||
| 3/16 |
Week 1. Introduction + Transformer - Course introduction - Resource accounting - Transformer |
0. Course Introduction 1. Resource accounting 2. Transformer |
1. Course Introduction + Resource accounting 2. Transformer |
| 3/23 |
Week 2. LLM Basics - Pre-training - Post-training - Fine-tuning, Prompting |
3. LLM Basics |
3-1. LLM Basics (1) 3-2. LLM Basics (2) |
| 3/30 |
Week 3. LLM Architecture (1) - Modern LLM models - Attention variants |
4. Modern LLM Architecture |
4-1. Modern LLM Architecture 4-2. Attention Variants |
| 4/6 |
Week 4. LLM Architecture (2) - Mixture-of-experts - Scaling Laws |
5. Mixture-of-Experts 6. Scaling Laws |
5. Mixture-of-Experts 6. Scaling Laws |
| 4/13 | No class | ||
| 4/20 |
Week 5. LLM Case Study - Recent model architectures |
7. LLM Case Study | 7. LLM Case Study |
| 4/27 |
Week 6. Understanding GPUs - GPUs - FlashAttention |
8. Understanding GPUs | 8. Understanding GPUs & FlashAttention |
| 5/4 |
Week 7. Parallelism - Multi-GPU/machine training |
9. Parallelism | 9. Parallelism |
| 5/11 |
Week 8. Inference, Evaluation - Inference cost & techniques - Evaluation metrics |
10. Inference 11. Evaluation |
10. Inference 11. Evaluation |
| 5/18 |
Week 9. Dataset, SFT - Training dataset - Supervised fine-tuning |
12. Dataset & SFT | 12. Dataset & SFT |
| 5/25 | No Class (National Holiday) | ||
| 6/1 |
Week 10. RLHF - Introduction to RL - RL from human feedback |
13. RLHF | 13. RLHF |
| 6/8 |
Week 11. Reasoning - Reasoning without Training - Training reasoning (RLVR) |
14. Reasoning | 14. Reasoning |
| 6/15 |
Week 12. Modern Post-Training - Modern approach - Case study - Agentic systems |