CONTENIDO:PART I. INFERENCE IN PROBABILISTIC MODELS
Probabilistic reasoning
Basic graph concepts
Belief networks
Graphical models
Efficient inference in trees
The junction tree algorithm
Making decisions
PART II. LEARNING IN PROBABILISTIC MODELS
Statistics for machine learning
Learning as inference
Naive Bayes
Learning with hidden variables
Bayesian model selection
PART III. MACHINE LEARNING:
Machine learning concepts
Nearest neighbour classification
Unsupervised linear dimension reduction
Supervised linear dimension reduction
Linear models
Bayesian linear models
Gaussian processes
Mixture models
Latent linear models
Latent ability models
PART IV. DYNAMICAL MODELS
Discrete-state Markov models
Continuous-state Markov models
Switching linear dynamical systems
Distributed computation
PART V. APPROXIMATE INFERENCE
Sampling
Deterministic approximate inference
Appendix. Background mathematics
Bibliography
Index
