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Machine learning

Introduction

Practices of machine learning.

  • Strategy
    • Loss function => Cost function
    • Experience Risk -> Expectation Risk
    • Structural Risk = Experience Rist + Regularization
    • k-fold cross validation
    • feature standardization
  • M.L Algorithm
    • Linear
    • Decision Tree
    • Naive Bayes
    • Support Vector Machine
    • Clustering
    • Neural Network => Deep Learning
    • Ensemble Learning
    • Reinforcement Learning
  • Optimization
    • Gradient descent
    • Newton method
    • Lagrange duality
    • Kernel method

Reference