Principles of Machine Learning

2015-03-07

 
Slides
PPT, PDF

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

Reference
  1. Slides by Hsuan-Tien Lin (NTU CSIE)