Template:Machine learning
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Part of a series on
Machine learning
and
data mining
Problems
Classification
Clustering
Regression
Anomaly detection
Data Cleaning
AutoML
Association rules
Reinforcement learning
Structured prediction
Feature engineering
Feature learning
Online learning
Semi-supervised learning
Unsupervised learning
Learning to rank
Grammar induction
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)
Clustering
BIRCH
CURE
Hierarchical
k
-means
Expectation–maximization (EM)
DBSCAN
OPTICS
Mean shift
Dimensionality reduction
Factor analysis
CCA
ICA
LDA
NMF
PCA
PGD
t-SNE
Structured prediction
Graphical models
Bayes net
Conditional random field
Hidden Markov
Anomaly detection
k
-NN
Local outlier factor
Artificial neural network
Autoencoder
Cognitive computing
Deep learning
DeepDream
Multilayer perceptron
RNN
LSTM
GRU
ESN
Restricted Boltzmann machine
GAN
SOM
Convolutional neural network
U-Net
Transformer
Vision
Spiking neural network
Memtransistor
Electrochemical RAM
(ECRAM)
Reinforcement learning
Q-learning
SARSA
Temporal difference (TD)
Multi-agent
Theory
Kernel machines
Bias–variance tradeoff
Computational learning theory
Empirical risk minimization
Occam learning
PAC learning
Statistical learning
VC theory
Machine-learning venues
NeurIPS
ICML
ML
JMLR
ArXiv:cs.LG
Related articles
Glossary of artificial intelligence
List of datasets for machine-learning research
Outline of machine learning
v
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