Mathematical analysis for machine learning and data mining

By:

Simovici, Dan

Material type: Text

TextPublication details: World Scientific Publishing Singapore 2023Description: xv, 968 pISBN:

9780000988898

Subject(s):

DDC classification:

006.312 SIM

Summary: This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The mathematical analysis component of the typical mathematical curriculum for computer science students omits these very important ideas and techniques which are indispensable for approaching specialized area of machine learning centered around optimization such as support vector machines, neural networks, various types of regression, feature selection, and clustering. The book is of special interest to researchers and graduate students who will benefit from these application areas discussed in the book. (https://www.worldscientific.com/worldscibooks/10.1142/10702#t=aboutBook)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.312 SIM (Browse shelf(Opens below))	1	Available		006435

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Previous	006.312 KUM Knowledge engineering	006.312 MAH Introduction to data science:	006.312 MOR Be data literate:	006.312 SIM Mathematical analysis for machine learning and data mining	006.312 TAN Introduction to data mining	006.312 ZAF Social media mining: an introduction	006.312 ZAK Data mining and machine learning:	Next

This compendium provides a self-contained introduction to mathematical analysis in the field of machine learning and data mining. The mathematical analysis component of the typical mathematical curriculum for computer science students omits these very important ideas and techniques which are indispensable for approaching specialized area of machine learning centered around optimization such as support vector machines, neural networks, various types of regression, feature selection, and clustering. The book is of special interest to researchers and graduate students who will benefit from these application areas discussed in the book.

(https://www.worldscientific.com/worldscibooks/10.1142/10702#t=aboutBook)

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