User:Barney4Blue/Books/ml

Machine Learning

 * Statistics
 * Conditional probability distribution
 * Covariance
 * Inference
 * Latent variable
 * Likelihood-ratio test
 * Log probability
 * Maximum likelihood
 * Mixture model
 * Prior probability
 * Random variable
 * Statistical classification
 * Statistical model


 * Machine Learning
 * Activation function
 * AdaBoost
 * Artificial intelligence
 * Artificial neural network
 * Artificial neuron
 * Backfitting algorithm
 * Backpropagation
 * Bayesian network
 * Binary classification
 * Boltzmann machine
 * Boosting (machine learning)
 * Cluster analysis
 * Convolutional neural network
 * Decision tree
 * Decision tree learning
 * Deep belief network
 * Deep learning
 * Discriminative model
 * Dropout (neural networks)
 * Early stopping
 * Ensemble learning
 * Extreme learning machine
 * Feedforward neural network
 * Generative model
 * Gradient boosting
 * Gradient descent
 * Greedy algorithm
 * Hyperbolic function
 * K-nearest neighbors algorithm
 * Linear classifier
 * Linear regression
 * Linear separability
 * Logistic function
 * Logistic regression
 * Machine learning
 * Multiclass classification
 * Multilayer perceptron
 * Naive Bayes classifier
 * Perceptron
 * Q-learning
 * Rectifier (neural networks)
 * Recurrent neural network
 * Regression analysis
 * Regularization (mathematics)
 * Reinforcement learning
 * Restricted Boltzmann machine
 * Self-organizing map
 * Semi-supervised learning
 * Sigmoid function
 * Softmax function
 * Stochastic gradient descent
 * Stochastic neural network
 * Supervised learning
 * Support vector machine
 * Unsupervised learning
 * Wake-sleep algorithm


 * Validation
 * Bias of an estimator
 * Bias–variance tradeoff
 * Cross-validation (statistics)
 * Errors and residuals
 * Least squares
 * Loss function
 * Mean squared error
 * Overfitting
 * Root-mean-square deviation
 * Sensitivity and specificity
 * Test set
 * Type III error
 * Variance


 * Misc
 * Binary data
 * Bipartite graph
 * Collaborative filtering
 * Convolution
 * Cross entropy
 * Dimensionality reduction
 * Feature extraction
 * Feature learning
 * Gauss–Markov theorem
 * Gibbs sampling
 * Graphical model
 * Hidden Markov model
 * Markov chain
 * Markov chain Monte Carlo
 * Markov property
 * Markov random field
 * Monte Carlo method
 * Principal component analysis
 * Random field
 * Sparse matrix