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

• Machine Learning in Action
• https://cuijiahua.com/blog/ml/