ECE7115 Multimodal VLM (LLM)
Inha University Β· Spring 2026
This course provides an in-depth study of Large Language Models (LLMs) β the essential foundation for understanding Multimodal Vision-Language Models.
Topics include LLM architectures, training/inference pipelines, GPU systems, and post-training methods such as RLHF and RLVR. (but I will not provide VLM-related topcis, sorry!)
Note: This course is built upon Stanford CS336.
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 | |
| 5/11 |
Week 8. Inference, Evaluation - Inference cost & techniques - Evaluation metrics |
||
| 5/18 |
Week 9. Dataset, SFT - Training dataset - Supervised fine-tuning |
||
| 5/25 | No Class (National Holiday) | ||
| 6/1 |
Week 10. RLHF - Introduction to RL - RL from human feedback |
||
| 6/8 |
Week 11. Reasoning - Training-free reasoning - Training reasoning (RL with verifiable rewards) |
||
| 6/15 |
Week 12. Tool & Agent, Case Study - Tool use, multi-agent - Case study on post-training |