Outline of machine learning

• An academic discipline • A branch of science • An applied science • A subfield of computer science • A branch of artificial intelligence • A subfield of soft computing • Application of statistics Paradigms of machine learning • Supervised learning, where the model is trained on labeled data • Unsupervised learning, where the model tries to identify patterns in unlabeled data • Reinforcement learning, where the model learns to make decisions by receiving rewards or penalties. == Applications of machine learning ==

• Applications of machine learning • Bioinformatics • Biomedical informatics • Computer vision • Customer relationship management • Data mining • Earth sciences • Email filtering • Inverted pendulum (balance and equilibrium system) • Natural language processing • Named Entity Recognition • Automatic summarization • Automatic taxonomy construction • Dialog system • Grammar checker • Language recognition • Handwriting recognition • Optical character recognition • Speech recognition • Text to Speech Synthesis • Speech Emotion Recognition • Machine translation • Question answering • Speech synthesis • Text mining • Term frequency–inverse document frequency • Text simplification • Pattern recognition • Facial recognition system • Handwriting recognition • Image recognition • Optical character recognition • Speech recognition • Recommendation system • Collaborative filtering • Content-based filtering • Hybrid recommender systems • Search engine • Search engine optimization • Social engineering == Machine learning hardware ==

• Graphics processing unit • Tensor processing unit • Vision processing unit == Machine learning tools ==

• Comparison of machine learning software • Comparison of deep learning software Machine learning frameworks Proprietary machine learning frameworks • Amazon Machine Learning • Microsoft Azure Machine Learning Studio • DistBelief (replaced by TensorFlow) Open source machine learning frameworks • Apache Singa • Apache MXNet • Caffe • PyTorch • mlpack • TensorFlow • Torch • CNTK • Accord.Net • Jax • MLJ.jl – A machine learning framework for Julia Machine learning libraries • Deeplearning4j • Theano • scikit-learn • Keras Machine learning algorithms • AdaBoost • Almeida–Pineda recurrent backpropagation • ALOPEX • Backpropagation • Bootstrap aggregating • CN2 algorithm • Constructing skill trees • Decision tree learning • Dehaene–Changeux model • Diffusion map • Dominance-based rough set approach • Dynamic time warping • Error-driven learning • Evolutionary multimodal optimization • Expectation–maximization algorithm • FastICA • Forward–backward algorithm • GeneRec • Genetic Algorithm for Rule Set Production • Growing self-organizing map • Hyper basis function network • IDistance • k-means clustering • k-nearest neighbors algorithm • Kernel methods for vector output • Kernel principal component analysis • Learning vector quantization • Leabra • Linde–Buzo–Gray algorithm • Local outlier factor • Logic learning machine • LogitBoost • Manifold alignment • Markov chain Monte Carlo (MCMC) • Minimum redundancy feature selection • Mixture of experts • Multiple kernel learning • Naive Bayes classifier • Non-negative matrix factorization • Online machine learning • Out-of-bag error • Prefrontal cortex basal ganglia working memory • PVLV • Q-learning • Quadratic unconstrained binary optimization • Query-level feature • Quickprop • Radial basis function network • Random forest • Randomized weighted majority algorithm • Reinforcement learning • Repeated incremental pruning to produce error reduction (RIPPER) • Rprop • Rule-based machine learning • Self-organizing map • Skill chaining • Sparse PCA • State–action–reward–state–action • Stochastic gradient descent • Structured kNN • Support vector machine • T-distributed stochastic neighbor embedding • Temporal difference learning • Wake-sleep algorithm • Weighted majority algorithm (machine learning) == Machine learning methods ==

Instance-based algorithm • K-nearest neighbors algorithm (KNN) • Learning vector quantization (LVQ) • Self-organizing map (SOM) === Regression analysis === • Logistic regression • Ordinary least squares regression (OLSR) • Linear regression • Stepwise regression • Multivariate adaptive regression splines (MARS) • Regularization algorithm • Ridge regression • Least Absolute Shrinkage and Selection Operator (LASSO) • Elastic net • Least-angle regression (LARS) • Classifiers • Probabilistic classifier • Naive Bayes classifier • Binary classifier • Linear classifier • Hierarchical classifier Dimensionality reduction Dimensionality reduction • Canonical correlation analysis (CCA) • Factor analysis • Feature extraction • Feature selection • Independent component analysis (ICA) • Linear discriminant analysis (LDA) • Multidimensional scaling (MDS) • Non-negative matrix factorization (NMF) • Partial least squares regression (PLSR) • Principal component analysis (PCA) • Principal component regression (PCR) • Projection pursuit • Sammon mapping • t-distributed stochastic neighbor embedding (t-SNE) Ensemble learning Ensemble learning • AdaBoost • Boosting • Bootstrap aggregating (also "bagging" or "bootstrapping") • Ensemble averaging • Gradient boosted decision tree (GBDT) • Gradient boosting • Random Forest • Stacked Generalization Meta-learning Meta-learning • Inductive bias • Metadata Reinforcement learning Reinforcement learning • Q-learning • State–action–reward–state–action (SARSA) • Temporal difference learning (TD) • Learning Automata Supervised learning Supervised learning • Averaged one-dependence estimators (AODE) • Artificial neural network • Case-based reasoning • Gaussian process regression • Gene expression programming • Group method of data handling (GMDH) • Inductive logic programming • Instance-based learning • Lazy learning • Learning Automata • Learning Vector Quantization • Logistic Model Tree • Minimum message length (decision trees, decision graphs, etc.) • Nearest Neighbor Algorithm • Analogical modeling • Probably approximately correct learning (PAC) learning • Ripple down rules, a knowledge acquisition methodology • Symbolic machine learning algorithms • Support vector machines • Random Forests • Ensembles of classifiers • Bootstrap aggregating (bagging) • Boosting (meta-algorithm) • Ordinal classification • Conditional Random Field • ANOVA • Quadratic classifiers • k-nearest neighbor • Boosting • SPRINT • Bayesian networks • Naive Bayes • Hidden Markov models • Hierarchical hidden Markov model Bayesian Bayesian statistics • Bayesian knowledge base • Naive Bayes • Gaussian Naive Bayes • Multinomial Naive Bayes • Averaged One-Dependence Estimators (AODE) • Bayesian Belief Network (BBN) • Bayesian Network (BN) Decision tree algorithms Decision tree algorithm • Decision tree • Classification and regression tree (CART) • Iterative Dichotomiser 3 (ID3) • C4.5 algorithm • C5.0 algorithm • Chi-squared Automatic Interaction Detection (CHAID) • Decision stump • Conditional decision tree • ID3 algorithm • Random forest • SLIQ Linear classifier Linear classifier • Fisher's linear discriminant • Linear regression • Logistic regression • Multinomial logistic regression • Naive Bayes classifier • Perceptron • Support vector machine Unsupervised learning Unsupervised learning • Expectation-maximization algorithm • Vector Quantization • Generative topographic map • Information bottleneck method • Association rule learning algorithms • Apriori algorithm • Eclat algorithm Artificial neural networks Artificial neural network • Feedforward neural network • Extreme learning machine • Convolutional neural network • Recurrent neural network • Long short-term memory (LSTM) • Logic learning machine • Self-organizing map Association rule learning Association rule learning • Apriori algorithm • Eclat algorithm • FP-growth algorithm Hierarchical clustering Hierarchical clustering • Single-linkage clustering • Conceptual clustering Cluster analysis Cluster analysis • BIRCH • DBSCAN • Expectation–maximization (EM) • Fuzzy clustering • Hierarchical clustering • k-means clustering • k-medians • Mean-shift • OPTICS algorithm Anomaly detection Anomaly detection • k-nearest neighbors algorithm (k-NN) • Local outlier factor Semi-supervised learning Semi-supervised learning • Active learning • Generative models • Low-density separation • Graph-based methods • Co-training • Transduction Deep learning Deep learning • Deep belief networks • Deep Boltzmann machines • Deep Convolutional neural networks • Deep Recurrent neural networks • Hierarchical temporal memory • Generative Adversarial Network • Style transfer • Transformer • Stacked Auto-Encoders Other machine learning methods and problems • Anomaly detection • Association rules • Bias-variance dilemma • Classification • Multi-label classification • Clustering • Data Pre-processing • Empirical risk minimization • Feature engineering • Feature learning • Learning to rank • Occam learning • Online machine learning • PAC learning • Regression • Reinforcement Learning • Semi-supervised learning • Statistical learning • Structured prediction • Graphical models • Bayesian network • Conditional random field (CRF) • Hidden Markov model (HMM) • Unsupervised learning • VC theory == Machine learning research ==

• List of artificial intelligence projects • List of datasets for machine learning research == History of machine learning ==

History of machine learning • Timeline of machine learning == Machine learning projects ==

Machine learning projects: • DeepMind • Google Brain • OpenAI • Meta AI • Hugging Face == Machine learning organizations ==

Machine learning conferences and workshops • Artificial Intelligence and Security (AISec) (co-located workshop with CCS) • Conference on Neural Information Processing Systems (NIPS) • ECML PKDD • International Conference on Machine Learning (ICML) • ML4ALL (Machine Learning For All) == Machine learning publications ==

Books on machine learning • Mathematics for Machine Learning • Hands-On Machine Learning Scikit-Learn, Keras, and TensorFlow • The Hundred-Page Machine Learning Book Machine learning journals • Machine Learning • Journal of Machine Learning Research (JMLR) • Neural Computation == Persons influential in machine learning ==

• Alberto Broggi • Andrei Knyazev • Andrew McCallum • Andrew Ng • Anuraag Jain • Armin B. Cremers • Ayanna Howard • Barney Pell • Ben Goertzel • Ben Taskar • Bernhard Schölkopf • Brian D. Ripley • Christopher G. Atkeson • Corinna Cortes • Demis Hassabis • Douglas Lenat • Eric Xing • Ernst Dickmanns • Geoffrey Hinton • Hans-Peter Kriegel • Hartmut Neven • Heikki Mannila • Ian Goodfellow • Jacek M. Zurada • Jaime Carbonell • Jeremy Slovak • Jerome H. Friedman • John D. Lafferty • John Platt • Julie Beth Lovins • Jürgen Schmidhuber • Karl Steinbuch • Katia Sycara • Leo Breiman • Lise Getoor • Luca Maria Gambardella • Léon Bottou • Marcus Hutter • Mehryar Mohri • Michael Collins • Michael I. Jordan • Michael L. Littman • Nando de Freitas • Ofer Dekel • Oren Etzioni • Pedro Domingos • Peter Flach • Pierre Baldi • Pushmeet Kohli • Ray Kurzweil • Rayid Ghani • Ross Quinlan • Salvatore J. Stolfo • Sebastian Thrun • Selmer Bringsjord • Sepp Hochreiter • Shane Legg • Stephen Muggleton • Steve Omohundro • Tom M. Mitchell • Trevor Hastie • Vasant Honavar • Vladimir Vapnik • Yann LeCun • Yasuo Matsuyama • Yoshua Bengio • Zoubin Ghahramani == See also ==