此工具包涵盖了模式识别和机器学习领域的重要算法和很多新的算法，由这些领域资深专家编写，并有相关教材出版，绝对不负众望。此工具包包括以下内容： * PCA * Mixtures of probabilistic PCA * Gaussian mixture model with EM training algorithm * Linear and logistic regression with IRLS training algorithm * Multi-layer perceptron with linear, logistic and softmax outputs and appropriate error functions * Radial basis function (RBF) networks with both Gaussian and non-local basis functions * Optimisers, including quasi-Newton methods, conjugate gradients and scaled conjugate gradient s * Multi-layer perceptron with Gaussian mixture outputs (mixture density networks) * Gaussian prior distributions over parameters for the MLP, RBF and GLM including multiple hyper-parameters * Laplace approximation framework for Bayesian inference (evidence procedure) * Automatic Relevance Determination for input selection * Markov chain Monte-Carlo including simple Metropolis and hybrid Monte-Carlo * K-nearest neighbour classifier * K-means clustering * Generative Topographic Map * Neuroscale topographic projection * Gaussian Processes * Hinton diagrams for network weights * Self-organising map s * Multi-layer perceptron with Gaussian mixture outputs (mixture density networks) * Gaussian prior distributions over parameters for the MLP, RBF and GLM including multiple hyper-parameters * Laplace approximation framework for Bayesian inference (evidence procedure) * Automatic Relevance Determination for input selection * Markov chain Monte-Carlo including simple Metropolis and hybrid Monte-Carlo * K-nearest neighbour classifier * K-means clustering * Generative Topographic Map * Neuroscale topographic projection * Gaussian Processes * Hinton diagrams for network weights * Self-organising map