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글은 「서울대학교 삼성 AI Expert 과정」강의 내용을 바탕으로 정리한 글입니다.

AI 입문, 신경망 아키텍처,  NLP기초부터 LLM 활용, 음성 처리까지 다양한 주제를 다룹니다.

전통적 ML에서 DNN 진화
machine learning to deep learning overview showing progression from basic models to multi-layer neural networks
언어의 이해와 생성
NLP overview graphic showing key language processing tasks such as tokenization parsing and semantic understanding
  • Traditional NLP
  • Foundations of NLP
  • Generation
  • Pretraining
  • Recommendation
  • RAG
Vision 이해와 분석
computer vision overview diagram showing image features extracted for visual recognition
  • Traditional Vision
  • Foundation
  • Classification
  • Detection
  • Segmentation
  • Generation
복합 데이터 동시 학습
multimodal AI illustration showing combined processing of text images and audio inputs
  • Photography
  • Camera Model
  • 3D Reconstruction
  • Motion & Tracking
  • Image Retrieval
  • Multimodal AI
음성 인식과 합성
audio processing overview showing waveform features used for sound classification tasks
  • Introduction
  • Speech Recognition
  • Deep Speech
환경과 상호작용하는 AI
reinforcement learning diagram showing agent actions environment feedback and reward signals
  • Introduction
  • Q-Learning
  • DQN
  • Actor-Critic Method
  • DDPG
  • TRPO
모델 경량화 및 최적화
optimization overview diagram showing gradient-based methods improving model performance
  • Introduction
  • Knowledge Distillation
  • Quantization
  • Pruning
  • Parallelization
학습부터 일반화까지
diagram showing a machine learning training pipeline with iterative updates
  • Introduction
  • Information Theory
  • Types of Learning
최적화 하드웨어 기술
machine learning hardware overview showing compute components used for training and inference
  • Introduction
  • Accelerator Types
  • GPU MicroArchitecture
  • In-Memory Computing