Machine Learning

The Wolfram Language includes a wide range of state-of-the-art integrated machine learning capabilities, from highly automated functions like (RefLink[Predict, paclet:ref/Predict]) and (RefLink[Classify, paclet:ref/Classify]) to functions based on specific methods and diagnostics, including the latest neural net approaches. The functions work on many types of data, including numerical, categorical, time series, textual, image and audio.

Learning from Input and Output

(RefLink[Classify, paclet:ref/Classify]) — classify data into categories using a built-in classifier or learning from examples

(RefLink[Predict, paclet:ref/Predict]) — predict values from data using a built-in predictor or learning from examples

(RefLink[ClassifierFunction, paclet:ref/ClassifierFunction]) — symbolic representation of a classifier to be applied to data

(RefLink[PredictorFunction, paclet:ref/PredictorFunction]) — symbolic representation of a predictor to be applied to data

(RefLink[ClassifierMeasurements, paclet:ref/ClassifierMeasurements]), (RefLink[PredictorMeasurements, paclet:ref/PredictorMeasurements]) — performance on test data

Learning from Sequences

(RefLink[SequencePredict, paclet:ref/SequencePredict]) — predict subsequent elements from sequence examples

(RefLink[SequencePredictorFunction, paclet:ref/SequencePredictorFunction]) — symbolic representation of a sequence predictor

Specific Methods

Automated Structure Discovery

(RefLink[LearnDistribution, paclet:ref/LearnDistribution]) — learn the underlying distribution of any type of data

(RefLink[DimensionReduction, paclet:ref/DimensionReduction]) — find how to project data onto lower-dimensional space

(RefLink[FeatureExtraction, paclet:ref/FeatureExtraction]) — find how to extract features from data

(RefLink[ClusterClassify, paclet:ref/ClusterClassify]) — classify data into clusters

(RefLink[FindDistribution, paclet:ref/FindDistribution]) — find a representation for data in terms of named distributions

Visualization

(RefLink[FeatureSpacePlot, paclet:ref/FeatureSpacePlot]) — visualize dimension-reduced feature space in 2D

(RefLink[FeatureSpacePlot3D, paclet:ref/FeatureSpacePlot3D]) — visualize dimension-reduced feature space in 3D

(RefLink[Dendrogram, paclet:ref/Dendrogram]) — visualize hierarchical clusters

Structure-Oriented Data Processing

(RefLink[AnomalyDetection, paclet:ref/AnomalyDetection]) — learn an anomaly detector function from data

(RefLink[SynthesizeMissingValues, paclet:ref/SynthesizeMissingValues]) — fill in missing values by imputing from existing data

Specific Methods

Learning from Actions

(RefLink[BayesianMinimization, paclet:ref/BayesianMinimization]) — model-based minimization of arbitrary objective functions

(RefLink[ActiveClassification, paclet:ref/ActiveClassification]) — learn a classifier by actively probing a system

(RefLink[ActivePrediction, paclet:ref/ActivePrediction]) — learn a predictor by actively probing a system

(OrangeLink[Neural Networks », paclet:guide/NeuralNetworks])

(RefLink[NetGraph, paclet:ref/NetGraph]) — represent an arbitrary neural network structure

(RefLink[NetTrain, paclet:ref/NetTrain]) — train any neural network on CPUs, GPUs, etc.

(RefLink[NetModel, paclet:ref/NetModel]) — collection of trained and untrained models

Machine Learning Options

(RefLink[FeatureExtractor, paclet:ref/FeatureExtractor]) — how to extract features to learn from

(RefLink[FeatureTypes, paclet:ref/FeatureTypes]) — feature types to assume for input data

(RefLink[PerformanceGoal, paclet:ref/PerformanceGoal]) — whether to optimize for memory, quality, or speed

(RefLink[TimeGoal, paclet:ref/TimeGoal]) — how long to allocate for training, etc.

(RefLink[RandomSeeding, paclet:ref/RandomSeeding]) — how to seed randomization

(RefLink[ComputeUncertainty, paclet:ref/ComputeUncertainty]) — return values including uncertainty (as (RefLink[Around, paclet:ref/Around]))

(RefLink[MissingValuePattern, paclet:ref/MissingValuePattern]) — specify how missing values are represented in data

(OrangeLink[Preparing Data », paclet:guide/DataTransformsAndSmoothing])

(RefLink[DeleteMissing, paclet:ref/DeleteMissing]) — delete missing elements in data

(RefLink[Standardize, paclet:ref/Standardize]) — transform data to have zero mean and unit variance

(OrangeLink[Filtering Data », paclet:guide/LinearAndNonlinearFilters])

(RefLink[MovingAverage, paclet:ref/MovingAverage]) — compute moving averages of lists, time series, etc.

Machine Vision Applications

(RefLink[ImageIdentify, paclet:ref/ImageIdentify]) — recognize objects in images

Natural Language Processing Applications

(RefLink[FindTextualAnswer, paclet:ref/FindTextualAnswer]) — find answers to questions from text

Creative Applications

(RefLink[ImageRestyle, paclet:ref/ImageRestyle]) — restyle an image according to samples