ML 스터디 계획

Week1 - Orientation

• 환경 설정 (Git, Colab)
• 교재, 일정 조율
• ML intro

Week2 - Model Training

• Least Square
• Pseudo-inverse (SVD)
• Gradient Descent & Convexity
• Newton method vs Gradient Descent
• Regulization - Ridge, Lasso
• Regression
• Classification

Week3 - Neural Network

• NN Simulation
• Activation Function
• Vanishing Gradient
• SGD
• Back Propagation
• Optimizer

Week4 - CNN & RNN

• CNN intro
• RNN intro

Week5 - Dimension Reduction & Unsupervised Learning

• PCA (SVD)
• KNN
• DBSCAN

Week6 - SVM

• Lagrange Multiplier
• Hard Margin SVM
• Soft Margin SVM
• Kernel trick

Week7 - Ensemble Learning

• Decision Tree
• Ensemble

Week8 - Natural Language Processing

• RNN review
• Attention
• Transformer

Week9 - Auto Encoder

• AE
• VAE
• GAN
• AAE

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