ML的基础公式

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都是一些基础公式。

  • KNN
  • Expected Conditional Entropy ，Cross Entropy Loss
  • Bias Variance Decomposition
  • Bagging with Generating Distribution，Bootstrap Aggregation，AdaBoost Key Concept，AdaBoost Algorithm
  • Random Forest
  • Bayes Optimality ，Na¨ıve Bayes，Gaussian Discriminant Analysis
  • Gradient Descent
  • Multi-class Classification
  • Activation Functions
  • Support Vector Machines
    https://tingfengx.github.io/uoftnotes/2019F/CSC311/intro_ml.pdf