Mathematical analysis for machine learning and data mining/ Dan Simovici
Publication details: Singapore: World Scientific, 2018Description: xv, 968 pages, 23cmISBN:- 9789813229686
- 23 006.3101515 Si593
| Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 006.3101515 Si593 (Browse shelf(Opens below)) | Available | 138443 |
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| 006.31 Y51 Foundations of quantum programming / | 006.31 Z63 Machine learning applications in software engineering | 006.31015 D329 Mathematics for machine learning/ | 006.3101515 Si593 Mathematical analysis for machine learning and data mining/ | 006.31015195 Ar658 A computational approach to statistical learning/ | 006.3101570 R178 Deep learning for the life sciences: applying deep learning to genomics microscopy drug discovery and more/ | 006.310727 C495 On adversarial robustness of deep learning systems/ |
Includes bibliographical references and index
Preface --
Part I. Set-Theoretical 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 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.
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