Mathematical Analysis for Machine Learning and Data Mining

Nonfiction, Computers, Advanced Computing, Theory, Database Management, General Computing
Cover of the book Mathematical Analysis for Machine Learning and Data Mining by Dan Simovici, World Scientific Publishing Company
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Dan Simovici ISBN: 9789813229709
Publisher: World Scientific Publishing Company Publication: May 21, 2018
Imprint: WSPC Language: English
Author: Dan Simovici
ISBN: 9789813229709
Publisher: World Scientific Publishing Company
Publication: May 21, 2018
Imprint: WSPC
Language: English

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.

Contents:

  • Set-Theoretical and Algebraic Preliminaries:

    • Preliminaries
    • Linear Spaces
    • Algebra of Convex Sets
  • Topology:

    • Topology
    • Metric Space Topologies
    • Topological Linear Spaces
  • Measure and Integration:

    • Measurable Spaces and Measures
    • Integration
  • Functional Analysis and Convexity:

    • Banach Spaces
    • Differentiability of Functions Defined on Normed Spaces
    • Hilbert Spaces
  • Applications:

    • Optimization
    • Iterative Algorithms
    • Neural Networks
    • Regression
    • Support Vector Machines

Readership: Researchers, academics, professionals and graduate students in artificial intelligence, and mathematical modeling.
0

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

Contents:

Readership: Researchers, academics, professionals and graduate students in artificial intelligence, and mathematical modeling.
0

More books from World Scientific Publishing Company

Cover of the book Diagnostics for a Globalized World by Dan Simovici
Cover of the book Canonical Quantum Gravity by Dan Simovici
Cover of the book New Perspectives on Einstein's E = mc² by Dan Simovici
Cover of the book Metals and Energy Finance by Dan Simovici
Cover of the book Global Credit Review by Dan Simovici
Cover of the book Saving America's Beaches by Dan Simovici
Cover of the book Intracranial Pressure and its Effect on Vision in Space and on Earth by Dan Simovici
Cover of the book Ethnic Chinese Business in Asia by Dan Simovici
Cover of the book Educating for Empathy by Dan Simovici
Cover of the book Enjoy Writing Your Science Thesis or Dissertation! by Dan Simovici
Cover of the book The Economies of China and India by Dan Simovici
Cover of the book Fundamentals of Network Biology by Dan Simovici
Cover of the book Competitiveness Analysis and Development Strategies for 33 Indonesian Provinces by Dan Simovici
Cover of the book Frontiers in Time Scales and Inequalities by Dan Simovici
Cover of the book Topological Phase Transitions and New Developments by Dan Simovici
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy