Pattern recognition and machine learning

Introduction. Example : polynomial curve fitting ; Probability theory ; Model selection ; The curse of dimensionality Decision theory ; Information theory --
Probability distributions. Binary vehicles ; Multinomial variables ; The Gaussian distribution ; The exponential family ; Nonparametric methods --
Linear models for regression. Linear basis function models ; The bias-variance decomposition ; Bayesian linear regression ; Bayesian model comparison ; The evidence approximation ; Limitations of fixed basis functions --
Linear models for classification. Discriminant functions ; Probabilistic generative models ; Probabilistic discrimitive models ; The Laplace approximation ; Bayesian logistic regression --
Neural networks. Feed-forward network functions ; Network training ; Error backpropagation ; The Hessian matrix ; Regularization in neural networks ; Mixture density networks ; Bayesian neural networks --
Kernel methods. Dual representations ; Constructing kernals ; Radial basis function networks ; Gaussian processes --
Sparse Kernel machines. Maximum margin classifiers ; Relevance vector machines --
Graphical models. Bayesian networks ; Conditional independence ; Markov random fields ; Inference in graphical models --
Mixture models and EM. K-means clustering ; Mixtures of Gaussians ; An alternative view of EM ; The EM algorithm in general --
Approximate inference. Variational inference ; Illustration : variational mixture of Gaussians ; Variational linear regression ; Exponential family distributions ; Local variational methods ; Variational logistic regression ; Expectation propagation --
Sampling methods. Basic sampling algorithms ; Markov chain Monte Carlo ; Gibbs sampling ; Slice sampling ; The hybrid Monte Carlo algorithm ; Estimating the partition function --
Continuous latent variables. Principal component analysis ; Probabilistic PCA ; Kernel PCA ; Nonlinear latent variable models --
Sequential data. Markoc models ; Hidden Markov models ; Linear dynamical systems --
Combining models. Bayesian model averaging ; Committees ; Boosting ; Tree-based models ; Conditional mixture models --
Data sets --
Probability distributions --
Properties of matrices --
Calculus of variations --
Lagrange multipliers.

The field of pattern recognition has undergone substantial development over the years. This book reflects these developments while providing a grounding in the basic concepts of pattern recognition and machine learning. It is aimed at advanced undergraduates or first year PhD students, as well as researchers and practitioners.