!!! Overview
[{$pagename}] are [Algorithms] used within a specific [Machine Learning model]

!! [Supervised Learning]
* [Logistic Regression] 
* [Backpropagation]

!! [Unsupervised Learning]
* [Apriori]
* [k-Means]

!! Regression Algorithms
The most popular regression algorithms are:
* [Ordinary Least Squares] Regression (OLSR)
* [Linear Regression]
* [Logistic Regression]
* Stepwise Regression
* [Multivariate Adaptive Regression Splines] ([MARS])
* Locally Estimated Scatterplot Smoothing (LOESS)

!! Instance-based Algorithms
Instance-based learning model is a decision problem with instances or examples of training data that are deemed important or required to the model.
The most popular instance-based algorithms are:
* [k-Nearest Neighbor] ([kNN])
* Learning Vector Quantization ([LVQ])
* Self-Organizing Map (SOM)
* Locally Weighted Learning ([LWL])

!! [Regularization] [Algorithms]
The most popular regularization algorithms are:
* [Ridge Regression]
* [Least Absolute Shrinkage and Selection Operator] ([LASSO])
* [Dropout Regularization]
* Elastic Net
* [Least-Angle Regression] ([LARS])



!! [Decision Tree] Algorithms
[Decision Tree] methods construct a model of decisions made based on actual values of [attributes] in the [data].

Decisions fork in tree structures until a prediction decision is made for a given record. Decision trees are trained on data for classification and regression problems. Decision trees are often fast and accurate and a big favorite in machine learning.

The most popular decision tree algorithms are:
* [Classification and Regression Tree] ([CART])
* Iterative Dichotomiser 3 (ID3)
* C4.5 and C5.0 (different versions of a powerful approach)
* Chi-squared Automatic Interaction Detection (CHAID)
* Decision Stump
* M5
* Conditional Decision Trees

!! [Bayesian Algorithms]
Bayesian AlgorithmsBayesian methods are those that explicitly apply Bayes’ Theorem for problems such as [classification] and [regression].

The most popular Bayesian algorithms are:
* [Naive Bayes]
* [Gaussian Naive Bayes]
* [Multinomial Naive Bayes]
* [Averaged One-Dependence Estimators] ([AODE])
* [Bayesian Belief Network] ([BBN])
* [Bayesian Network] ([BN])

!! [Cluster analysis] [Algorithms]
[Cluster analysis] [Algorithms], like [regression], describes the [Classification] of problem and the class of methods.

Clustering methods are typically organized by the modeling approaches such as centroid-based and [Hierarchical]. All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality.

The most popular clustering algorithms are:
* k-Means
* k-Medians
* Expectation Maximisation (EM)
* [Hierarchical] Clustering

!! Association Rule Learning Algorithms
Assoication Rule Learning AlgorithmsAssociation rule learning methods extract rules that best explain observed relationships between variables in data.

These rules can discover important and commercially useful associations in large multidimensional datasets that can be exploited by an organization.

The most popular association rule learning algorithms are:
* Apriori algorithm
* Eclat algorithm

!! [Artificial Neural network] Algorithms
[Artificial Neural network] Algorithms are models that are inspired by the structure and/or function of biological neural networks.

They are a class of pattern matching that are commonly used for [regression] and [classification] problems but are really an enormous subfield comprised of hundreds of algorithms and variations for all manner of problem types.

Note that I have separated out [Deep Learning] from neural networks because of the massive growth and popularity in the field. Here we are concerned with the more classical methods.

The most popular artificial neural network algorithms are:
* [Perceptron]
* [Backpropagation]
* Hopfield Network
* Radial Basis Function Network (RBFN)


!! [Deep Learning] Algorithms
[Deep Learning] methods are a modern update to [Artificial Neural networks] that exploit abundant cheap computation.

They are concerned with building much larger and more complex [Artificial Neural networks] and, as commented on above, many methods are concerned with semi-supervised learning problems where large datasets contain very little labeled data.

The most popular deep learning algorithms are:
* Deep Boltzmann Machine (DBM)
* Deep Belief Networks (DBN)
* [Convolutional Neural Network] ([CNN])
* Stacked Auto-Encoders

!! Dimensionality Reduction Algorithms
Dimensional Reduction Algorithms like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, but in this case in an [Unsupervised Learning] manner or order to summarize or describe data using less information.

This can be useful to visualize dimensional data or to simplify data which can then be used in a supervised learning method. Many of these methods can be adapted for use in [Classification Trees] and [Regression analysis].
* Principal Component Analysis (PCA)
* Principal Component Regression (PCR)
* Partial Least Squares Regression (PLSR)
* Sammon Mapping
* Multidimensional Scaling (MDS)
* Projection Pursuit
* Linear Discriminant Analysis (LDA)
* Mixture Discriminant Analysis (MDA)
* Quadratic Discriminant Analysis (QDA)
* Flexible Discriminant Analysis (FDA)

!! Ensemble Algorithms
Ensemble Algorithms are models composed of multiple weaker models that are independently trained and whose predictions are combined in some way to make the overall prediction.

Much effort is put into what types of weak learners to combine and the ways in which to combine them. This is a very powerful class of techniques and as such is very popular.
* Boosting
* Bootstrapped Aggregation (Bagging)
* AdaBoost
* Stacked Generalization (blending)
* Gradient Boosting Machines (GBM)
* Gradient Boosted Regression Trees (GBRT)
* Random Forest

!!Other Algorithms
Many [algorithms] were not covered.

For example, what group would Support Vector Machines go into? Its own?

I did not cover algorithms from specialty tasks in the process of machine learning, such as:
* Feature selection algorithms
* Algorithm accuracy evaluation
* Performance measures

I also did not cover algorithms from specialty subfields of machine learning, such as:
* Computational intelligence (evolutionary algorithms, etc.)
* [Computer vision] (CV)
* [Natural Language Processing] (NLP)
* Recommender Systems
* [Reinforcement learning]
* Graphical Models
And more…


!! More Information
There might be more information for this subject on one of the following:
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* [#1] - [A Tour of Machine Learning Algorithms|https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/|target='_blank'] - based on information obtained 2018-01-03- 
* [#2] - [Machine Learning Algorithms|Wikipedia:Outline_of_machine_learning#Machine_learning_algorithms|target='_blank'] - based on information obtained 2018-01-03-