Book#

  1. Programming_Collective_Intelligence
  2. Machine Learning(Chinese Version)

Basic Model#

  1. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition
  2. Generalized Linear Models and Logistic Regression
  3. An introduction to Generalized Linear Models 2nd
  4. Estimating a Dirichlet Distribution

Conditional Random Fields #

  1. An Introduction to Conditional Random Fields for Relational Learning
  2. Probabilistic Models for Segmenting and Labeling Sequence Data

Latent Semantic Analysis#

  1. Latent Semantic Analysis
  2. Probabilistic Latent Semantic Analysis

Latent Dirichlet Allocation#

  1. Latent Dirichlet Allocaton
  2. Parameter estimation for text analysis

Classic Algorithms#

  1. Expectation Maximization and Posterior Constraints
  2. Maximum Likelihood from Incomplete Data via the EM Algorithm
  3. Markov Chain Monte Carlo and Gibbs Sampling
  4. Explaining the Gibbs Sampler
  5. An introduction to MCMC for Machine Learning
  6. A Tutorial on spectral clustering
  7. A tutorial on Energy-based learning
  8. Understanding Belief Propagation and its Generalizations
  9. Construction free energy approximation and generalized belief propagation algorithms
  10. Loopy Belief Propagation for Approximate Inference An Empirical Study
  11. PageRank http://en.wikipedia.org/wiki/PageRank
  12. Adaboost http://en.wikipedia.org/wiki/AdaBoost

Theory#

  1. An introduction to Variational Methods for Graphical Models
  2. Probabilistic Networks
  3. Constructing Free Energy Approximations and Generalized Belief
  4. Tutorial on varational approximation methods
  5. A variational Bayesian framework for graphical models
  6. Elements of Information Theory 2nd

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