User:Dgoulier/Books/Wikipedia Machine Learning

Wikipedia Machine Learning

 * Accuracy paradox
 * Action model learning
 * Active learning (machine learning)
 * Adversarial machine learning
 * Algorithm Selection
 * Algorithmic inference
 * AlphaGo
 * Apprenticeship learning
 * Bag-of-words model
 * Ball tree
 * Base rate
 * Bayesian interpretation of kernel regularization
 * Bayesian optimization
 * Bayesian structural time series
 * Bias–variance tradeoff
 * Binary classification
 * Bing Predicts
 * Bongard problem
 * Bradley–Terry model
 * Catastrophic interference
 * Category utility
 * CBCL (MIT)
 * Computational learning theory
 * Concept learning
 * Conditional random field
 * Confusion matrix
 * Constrained conditional model
 * Coupled pattern learner
 * Cross-entropy method
 * Cross-validation (statistics)
 * Curse of dimensionality
 * Darkforest
 * Data pre-processing
 * Dataiku
 * Decision list
 * Deep feature synthesis
 * Deeplearning4j
 * DeepMind
 * Dimensionality reduction
 * Discriminative model
 * Document classification
 * Domain adaptation
 * Eager learning
 * Early stopping
 * Elastic matching
 * Empirical risk minimization
 * Ensembles of classifiers
 * Error Tolerance (PAC learning)
 * Evaluation of binary classifiers
 * Evolutionary programming
 * Evolvability (computer science)
 * Expectation propagation
 * Explanation-based learning
 * Feature (machine learning)
 * Feature engineering
 * Feature hashing
 * Feature learning
 * Feature scaling
 * Feature vector
 * Formal concept analysis
 * Generative model
 * Genetic algorithm
 * Hyperparameter optimization
 * Inductive bias
 * Inductive probability
 * Inductive programming
 * Inductive transfer
 * Inferential theory of learning
 * Instance selection
 * Instance-based learning
 * Instantaneously trained neural networks
 * Isotropic position
 * Kernel density estimation
 * Kernel embedding of distributions
 * Kernel random forest
 * Knowledge integration
 * Knowledge Vault
 * Large margin nearest neighbor
 * Lazy learning
 * Learnable function class
 * Learning automata
 * Learning to rank
 * Learning with errors
 * Leave-one-out error
 * Linear predictor function
 * Linear separability
 * List of datasets for machine learning research
 * Local case-control sampling
 * Logic learning machine
 * M-Theory (learning framework)
 * Machine learning
 * Machine learning control
 * Manifold regularization
 * Matrix regularization
 * Matthews correlation coefficient
 * Meta learning (computer science)
 * Mixture model
 * Mountain Car
 * Movidius
 * Multi-armed bandit
 * Multi-task learning
 * Multilinear principal component analysis
 * Multilinear subspace learning
 * Multiple instance learning
 * Multiple-instance learning
 * Multiplicative Weight Update Method
 * Multivariate adaptive regression splines
 * MysteryVibe
 * Native-language identification
 * Nearest neighbor search
 * Neural modeling fields
 * Occam learning
 * Offline learning
 * OpenNN
 * Outline of machine learning
 * Overfitting
 * Parity learning
 * Pattern language (formal languages)
 * Pattern recognition
 * Predictive learning
 * Predictive state representation
 * Preference learning
 * Prior knowledge for pattern recognition
 * Proactive learning
 * Probability matching
 * Product of experts
 * Proximal gradient methods for learning
 * Random indexing
 * Random projection
 * Relational data mining
 * Representer theorem
 * Sample complexity
 * Semantic analysis (machine learning)
 * Semantic folding
 * Semi-supervised learning
 * Sequence labeling
 * Similarity learning
 * Singular statistical model
 * Skymind
 * Solomonoff's theory of inductive inference
 * Sparse dictionary learning
 * Spike-and-slab variable selection
 * Stability (learning theory)
 * Statistical classification
 * Statistical learning theory
 * Statistical relational learning
 * Stochastic block model
 * Structural risk minimization
 * Structured sparsity regularization
 * Subclass reachability
 * Supervised learning
 * Test set
 * The Master Algorithm
 * Timeline of machine learning
 * Transduction (machine learning)
 * Trax Image Recognition
 * Ugly duckling theorem
 * Uncertain data
 * Uniform convergence in probability
 * Universal portfolio algorithm
 * Unsupervised learning
 * User behavior analytics
 * Vanishing gradient problem
 * Version space learning