Statistics and Machine Learning Toolbox provides functions and apps to describe, analyze, and model data. You can use descriptive statistics and plots for exploratory data analysis, fit probability distributions to data, generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models. For multidimensional data analysis, Statistics and Machine Learning Toolbox provides feature selection, stepwise regression, principal component analysis (PCA), regularization, and other dimensionality reduction methods that let you identify variables or features that impact your model. The toolbox provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models.

Celestion loudspeakers have powered the performances of many noted guitar and bass players, including legends such as Jimi Hendrix. Deciding whether a loudspeaker is good enough for professional musicians is a lengthy and painstaking process. Each speaker has its own unique sound based on a combination of sonic characteristics, such as midrange character and brightness. Evaluating a musical instrument loudspeaker involves subjective judgement about whether it generates a "good" sound. Only engineers with years of experience can reliably make that decision, and then only after repeated listening to a single loudspeaker and comparing the sounds it produces with those produced by a reference speaker.

When you study a painting, chances are that you can make several inferences about it. In addition to understanding the subject matter, for example, you may be able to classify it by period, style, and artist. Could a computer algorithm "understand" a painting well enough to perform these classification tasks as easily as a human being? We also addressed two other intriguing questions about the capabilities and limitations of AI algorithms: whether they can identify which paintings have had the greatest influence on later artists, and whether they can measure a painting's creativity using only its visual features. We wanted to develop algorithms capable of classifying large groups of paintings by style (for example, as Cubist, Impressionist, Abstract Expressionist, or Baroque), genre (for example, landscape, portrait, or still life), and artist. One requirement for this classification is the ability to recognize color, composition, texture, perspective, subject matter, and other visual features.