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A concise introduction to machine learning / Anita Faul.

By: Faul, A. C. (Anita C.) [author.].
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC machine learning & pattern recognition.Publisher: Boca Raton, Florida : CRC Press, [2019]Description: 1 online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9781351204750; 1351204750; 9781351204736; 1351204734; 9781351204743; 1351204742; 9781351204729; 1351204726.Subject(s): Machine learning -- Textbooks | BUSINESS & ECONOMICS / Statistics | COMPUTERS / General | COMPUTERS / Computer Graphics / Game Programming & DesignDDC classification: 006.3/1 Online resources: Taylor & Francis | OCLC metadata license agreement
Contents:
Introduction -- Probability theory -- Sampling -- Linear classification -- Non-linear classification -- Dimensionality reduction -- Regression -- Feature learning.
Summary: "Machine Learning is known by many different names, and is used in many areas of science. It is also used for a variety of applications, including spam filtering, optical character recognition, search engines, computer vision, NLP, advertising, fraud detection, robotics, data prediction, astronomy. Considering this, it can often be difficult to find a solution to a problem in the literature, simply because different words and phrases are used for the same concept. This class-tested textbook aims to alleviate this, using mathematics as the common language. It covers a variety of machine learning concepts from basic principles, and llustrates every concept using examples in MATLAB"-- Provided by publisher.
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"Machine Learning is known by many different names, and is used in many areas of science. It is also used for a variety of applications, including spam filtering, optical character recognition, search engines, computer vision, NLP, advertising, fraud detection, robotics, data prediction, astronomy. Considering this, it can often be difficult to find a solution to a problem in the literature, simply because different words and phrases are used for the same concept. This class-tested textbook aims to alleviate this, using mathematics as the common language. It covers a variety of machine learning concepts from basic principles, and llustrates every concept using examples in MATLAB"-- Provided by publisher.

Introduction -- Probability theory -- Sampling -- Linear classification -- Non-linear classification -- Dimensionality reduction -- Regression -- Feature learning.

OCLC-licensed vendor bibliographic record.

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