PRML (Pattern Recognition and Machine Learning)

  1. Introduction
  2. Probability Distributions
  3. Linear Models for Regression
  4. Linear Models for Classification
  5. Neural Networks
  6. Kernel Methods
  7. Sparse Kernel Machines
  8. Graphical Models
  9. Mixture Models and EM
  10. Approximate Interference
  11. Sampling Methods
  12. Continuous Latent Variables
  13. Sequential Data
  14. Combining Models