Machine Learning

General Supervised Learning

Classify — classify data into categories using a built-in classifier or learning from examples

ClassifierFunction — symbolic representation of a classifier to be applied to data

Predict — predict values from data using a built-in predictor or learning from examples

PredictorFunction — symbolic representation of a predictor to be applied to data

ClassifierMeasurements, PredictorMeasurements — performance on test data

ClassifierInformation, PredictorInformation — model information etc.

PerformanceGoal  ▪  Method  ▪  UtilityFunction  ▪  ClassPriors  ▪  IndeterminateThreshold

ImageIdentify, ImageInstanceQ — automatically recognize objects in images

Specific Methods for Supervised Learning

Nearest, NearestNeighborGraph — find nearest neighbors

FindFit — find a generalized nonlinear fit

LinearModelFit  ▪  LogitModelFit  ▪  NonlinearModelFit  ▪  GeneralizedLinearModelFit  ▪  ProbitModelFit

TimeSeriesModelFit — fit a wide variety of types of time series

Interpolation — find an interpolation of values in a dataset

FindFormula — find a simple symbolic formula for data

FindSequenceFunction — find a function to reproduce a discrete sequence

FindHiddenMarkovStates — find the most probable path in a Markov model

General Unsupervised Learning

DimensionReduce — project data onto lower-dimensional space

DimensionReduction  ▪  DimensionReducerFunction

FeatureExtraction — learn a feature extractor function from data

FeatureExtract  ▪  FeatureExtractorFunction

ClusterClassify — classify data into clusters

FindDistribution — find a simple symbolic distribution from data

Specific Methods Unsupervised Learning

Eigensystem  ▪  SingularValueDecomposition  ▪  PrincipalComponents  ▪  KarhunenLoeveDecomposition

FindClusters — find clusters in numerical, textual, image, etc. data

ClusteringTree  ▪  ClusteringComponents

FindGraphCommunities — find communities or clusters in graphs

SmoothKernelDistribution — find kernel density estimates for data

FindHiddenMarkovStates — infer hidden Markov states from a sequence of data

Reinforcement Learning & Optimization

BayesianMinimization — model-based minimization of arbitrary objective functions

Neural Networks »

NetGraph — represent an arbitrary neural network structure

NetChain  ▪  DotPlusLayer  ▪  ConvolutionLayer  ▪  PoolingLayer  ▪  ...

NetTrain — train any neural network on CPUs, GPUs, etc.

Machine Learning Options

FeatureExtractor — how to extract features to learn from

FeatureTypes — feature types to assume for input data

PerformanceGoal — whether to optimize for memory, quality, or speed

Preparing Data »

Standardize — transform data to have zero mean and unit variance

Clip  ▪  Rescale  ▪  Threshold  ▪  LogisticSigmoid  ▪  ImageAdjust

CountsBy  ▪  GroupBy  ▪  SortBy  ▪  DeleteDuplicates

Filtering Data »

MovingAverage — compute moving averages of lists, time series, etc.

GaussianFilter  ▪  MeanFilter  ▪  MeanShiftFilter  ▪  LowpassFilter  ▪  ...

Missing — symbolic representation of missing elements in data

• Cluster Analysis
• Statistical Model Analysis
• Data Transforms & Smoothing
• Computer Vision
• Social Network Analysis

• Wolfram Community: Machine Learning