Template:Machine learning

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Machine learning

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Machine learning and data mining
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Expand Problems Classification Regression Clustering dimension reduction density estimation Anomaly detection Data Cleaning AutoML Association rules Structured prediction Feature engineering Feature learning Online learning Reinforcement learning Supervised learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction
Expand Supervised learning (classification • regression) Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Expand Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Expand Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Expand Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Expand Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Expand Artificial neural network Autoencoder Cognitive computing Deep learning DeepDream Multilayer perceptron RNN LSTM GRU ESN reservoir computing Restricted Boltzmann machine GAN SOM Convolutional neural network U-Net Transformer Vision Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Expand Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Expand Learning with humans Active learning Crowdsourcing Human-in-the-loop
Expand Model diagnostics Learning curve
Expand Theory Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Expand Machine-learning venues NeurIPS ICML ICLR ML JMLR
Expand Related articles Glossary of artificial intelligence List of datasets for machine-learning research Outline of machine learning
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