A concise introduction to machine learning / Anita Faul.

By:

Faul, A. C. (Anita C.) [author.]

Material type: Text

TextSeries: Chapman & Hall/CRC machine learning & pattern recognitionPublisher: Boca Raton, Florida : CRC Press, [2019]Description: pages cmContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780815384205 (hbk : alk. paper)
9780815384106 (pbk : alk. paper)

Subject(s):

Machine learning -- Textbooks

DDC classification:

006.3/1 23

LOC classification:

Q325.5 .F38 2020

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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Item type	Current library	Call number	Status	Date due	Barcode
Books	Symbiosis School for Liberal Arts	006/FAU (Browse shelf(Opens below))	Available		SSLA-B-9880

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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.

Includes bibliographical references and index.

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

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