Mathematical Analysis for Machine Learning and Data Mining/ Dan Simovici
By: Simovici, Dan [author].
Publisher: Singapore: World Scientific, [2018]Description: xv, 968 pages, 23cm.ISBN: 9789813229686.Subject(s): Machine LearningMathematics  Data MiningMathematicsDDC classification: 006.3101515Item type  Current location  Call number  Status  Date due  Barcode  Item holds  

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ISI Library, Kolkata

006.3101515 Si593 (Browse shelf)  Available  138443 
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006.31 W345 Machine learning refined :  006.31 Y51 Foundations of quantum programming /  006.31 Z63 Machine learning applications in software engineering  006.3101515 Si593 Mathematical Analysis for Machine Learning and Data Mining/  006.312 Ad234 Data analysis and pattern recognition in multiple databases /  006.312 Ag266 Data mining :  006.312 Ah285 Practical guide to data mining for business and industry / 
Includes bibliographical references and index
Preface 
Part I. SetTheoretical and Algebraic Preliminaries 
Preliminaries 
Linear Spaces 
Algebra of Convex Sets 
Part II. Topology 
Topology 
Metric Space Topologies 
Topological Linear Spaces 
Part III. Measure and Integration 
Measurable Spaces and Measures 
Integration 
Part IV. Functional Analysis and Convexity 
Banach Spaces 
Differentiability of Functions Defined on Normed Spaces 
Hilbert Spaces 
Convex Functions 
Part V. Applications 
Optimization 
Iterative Algorithms 
Neural Networks 
Regression 
Support Vector Machines 
Bibliography 
Index.
This compendium provides a selfcontained 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.
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