ECE7115 Multimodal VLM (LLM)

인하대학교 · 2026년 봄학기

본 강의는 Multimodal VLM을 이해하기 위한 핵심 기반인 대규모 언어 모델(Large Language Model; LLM)을 심도 있게 다룹니다. Transformer부터 최신 LLM 모델 아키텍처, 학습/추론 파이프라인, GPU 시스템, RL 기반 사후 학습까지 LLM의 기초부터 최신 기술을 학습합니다. (강의 이름과 다르게 VLM 내용은 다루지 않습니다!)
참고: 본 강의는 Stanford CS336을 기반으로 구성되었습니다.

강의자: 안남혁 (인하대학교 전기전자공학부)

강의 스케쥴 & 자료

날짜	내용	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