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  

Books 
ISI Library, Kolkata

006.3101515 Si 593 (Browse shelf)  Available  138443 
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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