​​Principles of Machine Learning

​​2015-03-07

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​​Slides
​​PPT, PDF
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​​Outline
  • ​​Introduction to Machine Learning
  • ​​The Learning Model
  • ​​Theory of Generalization
  • ​​Hoeffding’s Inequality
  • ​​Vapnik-Chervonenkis (VC) Bound
  • ​​Noise & Error Measure
  • ​​Learning Algorithm
  • ​​Linear Classification
  • ​​Perceptron Learning Algorithm (PLA)
  • ​​Pocket Algorithm
  • ​​Linear Regression
  • ​​Logistic Regression
  • ​​Gradient Descent
  • ​​Stochastic Gradient Descent
  • ​​Hazard of Overfitting
  • ​​Regularization
  • ​​Validation
  • ​​Blending and Bagging
  • ​​Aggression
  • ​​Bootstrapping
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​​Reference
  1. ​​Slides by Hsuan-Tien Lin (NTU CSIE)
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