Table of Contents:
  • Introduction
  • Machine-learning foundations : the probabilistic framework
  • Probabilistic modeling and inference : examples
  • Machine learning algorithms
  • Neural networks : the theory
  • Neural networks : applications
  • Hidden Markov models: the theory
  • Hidden Markov models: applications
  • Probabilistic graphical models in bioinformatics
  • Probabilistic models of evolution : phylogenetic trees
  • Stochastic grammars and linguistics
  • Microarrays and gene expression
  • Internet resources and public databases
  • Statistics
  • Information theory, entropy, and relative entropy
  • Probabilistic graphical models
  • HMM technicalities, scaling, periodic architectures, state functions, and dirichlet mixtures
  • Gaussian processes, kernel methods and support vector machines
  • Symbols and abbreviations