Answers.Body: <p><strong>Supervised learning</strong></p>
  <ul>
  <li>ANOVA</li>
  <li>Averaged one-dependence estimators</li>
  <li>Artificial neural network
  <ul>
  <li>Convolutional neural network</li>
  <li>Extreme learning machine</li>
  <li>Feedforward neural network</li>
  <li>Logic learning machine</li>
  <li>Long short-term memory</li>
  <li>Recurrent neural network</li>
  <li>Self-organizing map</li>
  </ul>
  </li>
  <li>Bayesian networks</li>
  <li>Boosting</li>
  <li>Case-based reasoning</li>
  <li>Conditional random field</li>
  <li>Decision tree algorithms
  <ul>
  <li>C4.5 algorithm</li>
  <li>C5.0 algorithm</li>
  <li>Chi-squared automatic interaction detection</li>
  <li>Classification and regression tree</li>
  <li>Conditional decision tree</li>
  <li>Decision stump</li>
  <li>Decision tree</li>
  <li>ID3 algorithm</li>
  <li>Iterative dichotomiser 3</li>
  <li>Random forest</li>
  <li>SLIQ</li>
  </ul>
  </li>
  <li>Ensembles of classifiers
  <ul>
  <li>Bootstrap aggregating</li>
  <li>Boosting</li>
  </ul>
  </li>
  <li>Gaussian process regression</li>
  <li>Gene expression programming</li>
  <li>Group method of data handling</li>
  <li>Inductive logic programming</li>
  <li>Information fuzzy networks</li>
  <li>Instance-based learning</li>
  <li>K-nearest neighbour</li>
  <li>Lazy learning</li>
  <li>Learning vector quantization</li>
  <li>Linear
  <ul>
  <li>Elastic-net</li>
  <li>Lasso</li>
  <li>Linear discriminant analysis</li>
  <li>Linear regression</li>
  <li>Logistic regression</li>
  <li>Multinomial logistic regression</li>
  <li>Naive bayes classifier</li>
  <li>Ordinary least squares</li>
  <li>Passive aggressive algorithms</li>
  <li>Perceptron</li>
  <li>Polynomial regression</li>
  <li>Ridge regression / classification</li>
  <li>Support vector machine</li>
  </ul>
  </li>
  <li>Logistic model tree</li>
  <li>Minimum message length
  <ul>
  <li>Analogical modelling</li>
  <li>Nearest neighbour algorithm</li>
  </ul>
  </li>
  <li>Ordinal classification</li>
  <li>Probably approximately correct learning</li>
  <li>Quadratic classifiers</li>
  <li>Random forests</li>
  <li>Ripple down rules</li>
  <li>Symbolic machine learning</li>
  </ul>
  <p><strong>Semi-supervised learning</strong></p>
  <ul>
  <li>Active learning</li>
  <li>Co-training</li>
  <li>Graph-based methods</li>
  <li>Generative models</li>
  <li>Low-density separation</li>
  <li>Transduction</li>
  </ul>
  <p><strong>Unsupervised learning</strong></p>
  <ul>
  <li>Association rule learning
  <ul>
  <li>Apriori algorithm</li>
  <li>Eclat algorithm</li>
  <li>FP-growth algorithm</li>
  </ul>
  </li>
  <li>Auto-encoders</li>
  <li>Cluster analysis
  <ul>
  <li>BIRCH</li>
  <li>Conceptual clustering</li>
  <li>DBSCAN</li>
  <li>Expectation-maximization</li>
  <li>Fuzzy clustering</li>
  <li>Hierarchical clustering</li>
  <li>K-means clustering</li>
  <li>K-medians</li>
  <li>Mean-shift</li>
  <li>OPTICS algorithm</li>
  <li>Single-linkage clustering</li>
  </ul>
  </li>
  <li>Dimensionality reduction
  <ul>
  <li>Canonical correlation analysis</li>
  <li>Dynamic mode decomposition</li>
  <li>Factor analysis</li>
  <li>Feature extraction</li>
  <li>Feature selection</li>
  <li>Independent component analysis</li>
  <li>Linear discriminant analysis</li>
  <li>Multidimensional scaling</li>
  <li>Non-negative matrix factorization</li>
  <li>Partial least squares regression</li>
  <li>Principal component analysis</li>
  <li>Principal component regression</li>
  <li>Projection pursuit</li>
  <li>Sammon mapping</li>
  <li>T-distributed stochastic neighbour embedding</li>
  </ul>
  </li>
  <li>Expectation-maximization algorithm</li>
  <li>Generative topographic map</li>
  <li>Information bottleneck method</li>
  <li>Manifold learning</li>
  <li>Vector quantization</li>
  </ul>
  <p><strong>Reinforcement learning</strong></p>
  <ul>
  <li>Deterministic policy gradient</li>
  <li>Learning automata</li>
  <li>Proximal policy optimization</li>
  <li>Q-learning</li>
  <li>Soft actor-critic</li>
  <li>State–action–reward–state–action</li>
  <li>Temporal difference learning</li>
  <li>Trust region policy Optimization</li>
  </ul>
  <p><strong>Other</strong></p>
  <ul>
  <li>Bayesian belief network</li>
  <li>Bayesian knowledge base</li>
  <li>Deep belief networks</li>
  <li>Deep boltzmann machines</li>
  <li>Deep neural networks</li>
  <li>Discrepancy modelling</li>
  <li>Gaussian naive bayes</li>
  <li>Generative adversarial network</li>
  <li>Hierarchical temporal memory</li>
  <li>Knowledge-enhanced machine learning</li>
  <li>Markov models</li>
  <li>Multinomial naive bayes</li>
  <li>Neural style transfer</li>
  <li>Physics-informed machine learning</li>
  <li>Sparse identification of nonlinear dynamics</li>
  <li>Transformer</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-11-30T15:41:41.847Z
Answers.LastActivityDate: 2022-12-01T09:10:32.447Z
Answers.LastEditDate: 2022-12-01T09:10:32.447Z
Answers.UserDisplayName: Bernardo Costa
Body: <p>I could not find a comprehensive list of machine learning algorithms online.</p>
  <p>Can we try to make one here and improve and update it as needed?</p>
  <p>There are challenges in the algorithm definitions and how to group and organize them, but my preliminary list (built from e.g. <a href="https://en.wikipedia.org/wiki/Outline_of_machine_learning" rel="noreferrer">1</a>, <a href="https://scikit-learn.org/stable/supervised_learning.html" rel="noreferrer">2</a>, <a href="https://scikit-learn.org/stable/unsupervised_learning.html" rel="noreferrer">3</a>), is given in my answer below.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-11-30T18:52:02.643Z
Comments.Text: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
Comments.UserDisplayName: Community
Tags: <machine-learning>
Title: A comprehensive list of machine learning algorithms
UserDisplayName: Bernardo Costa
ThreadText: # A comprehensive list of machine learning algorithms
  
  (Asked by: username_0 on 2022-11-30)
  
  I could not find a comprehensive list of machine learning algorithms online.
  
  Can we try to make one here and improve and update it as needed?
  
  There are challenges in the algorithm definitions and how to group and organize them, but my preliminary list (built from e.g. [1](https://en.wikipedia.org/wiki/Outline_of_machine_learning), [2](https://scikit-learn.org/stable/supervised_learning.html), [3](https://scikit-learn.org/stable/unsupervised_learning.html)), is given in my answer below.
  
  **Tags:** machine-learning
  > **Comments:**
  > **username_1**: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2022-11-30. Score: 8
  
  **Supervised learning**
  
  * ANOVA
  * Averaged one-dependence estimators
  * Artificial neural network
    + Convolutional neural network
    + Extreme learning machine
    + Feedforward neural network
    + Logic learning machine
    + Long short-term memory
    + Recurrent neural network
    + Self-organizing map
  * Bayesian networks
  * Boosting
  * Case-based reasoning
  * Conditional random field
  * Decision tree algorithms
    + C4.5 algorithm
    + C5.0 algorithm
    + Chi-squared automatic interaction detection
    + Classification and regression tree
    + Conditional decision tree
    + Decision stump
    + Decision tree
    + ID3 algorithm
    + Iterative dichotomiser 3
    + Random forest
    + SLIQ
  * Ensembles of classifiers
    + Bootstrap aggregating
    + Boosting
  * Gaussian process regression
  * Gene expression programming
  * Group method of data handling
  * Inductive logic programming
  * Information fuzzy networks
  * Instance-based learning
  * K-nearest neighbour
  * Lazy learning
  * Learning vector quantization
  * Linear
    + Elastic-net
    + Lasso
    + Linear discriminant analysis
    + Linear regression
    + Logistic regression
    + Multinomial logistic regression
    + Naive bayes classifier
    + Ordinary least squares
    + Passive aggressive algorithms
    + Perceptron
    + Polynomial regression
    + Ridge regression / classification
    + Support vector machine
  * Logistic model tree
  * Minimum message length
    + Analogical modelling
    + Nearest neighbour algorithm
  * Ordinal classification
  * Probably approximately correct learning
  * Quadratic classifiers
  * Random forests
  * Ripple down rules
  * Symbolic machine learning
  
  **Semi-supervised learning**
  
  * Active learning
  * Co-training
  * Graph-based methods
  * Generative models
  * Low-density separation
  * Transduction
  
  **Unsupervised learning**
  
  * Association rule learning
    + Apriori algorithm
    + Eclat algorithm
    + FP-growth algorithm
  * Auto-encoders
  * Cluster analysis
    + BIRCH
    + Conceptual clustering
    + DBSCAN
    + Expectation-maximization
    + Fuzzy clustering
    + Hierarchical clustering
    + K-means clustering
    + K-medians
    + Mean-shift
    + OPTICS algorithm
    + Single-linkage clustering
  * Dimensionality reduction
    + Canonical correlation analysis
    + Dynamic mode decomposition
    + Factor analysis
    + Feature extraction
    + Feature selection
    + Independent component analysis
    + Linear discriminant analysis
    + Multidimensional scaling
    + Non-negative matrix factorization
    + Partial least squares regression
    + Principal component analysis
    + Principal component regression
    + Projection pursuit
    + Sammon mapping
    + T-distributed stochastic neighbour embedding
  * Expectation-maximization algorithm
  * Generative topographic map
  * Information bottleneck method
  * Manifold learning
  * Vector quantization
  
  **Reinforcement learning**
  
  * Deterministic policy gradient
  * Learning automata
  * Proximal policy optimization
  * Q-learning
  * Soft actor-critic
  * State–action–reward–state–action
  * Temporal difference learning
  * Trust region policy Optimization
  
  **Other**
  
  * Bayesian belief network
  * Bayesian knowledge base
  * Deep belief networks
  * Deep boltzmann machines
  * Deep neural networks
  * Discrepancy modelling
  * Gaussian naive bayes
  * Generative adversarial network
  * Hierarchical temporal memory
  * Knowledge-enhanced machine learning
  * Markov models
  * Multinomial naive bayes
  * Neural style transfer
  * Physics-informed machine learning
  * Sparse identification of nonlinear dynamics
  * Transformer
  
  
  ---
  
  