Introduction to Algorithms for Data Mining and Machine Learning

Xin-She Yang

• Publisher: Academic Press
• Year: 2019
• Print ISBN: 9780128172162
• eBook ISBN: 9780128172179
• Topic: Mathematics
• Subtopic: Applied Mathematics
• Index: Mathematics

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data.- Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics- Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study- Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages

1

Introduction to optimization

Abstract

This chapter introduces the fundamentals of algorithms in the context of data mining, optimization, and machine learning, including the feasibility, constraints, optimality, Lagrange multipliers, KKT conditions, and gradient-based techniques.

Keywords

Algorithm; data mining; gradient; machine learning; optimization

Chapter Outline

• 1.1 Algorithms
  • 1.1.1 Essence of an algorithm
  • 1.1.2 Issues with algorithms
  • 1.1.3 Types of algorithms
• 1.2 Optimization
  • 1.2.1 A simple example
  • 1.2.2 General formulation of optimization
  • 1.2.3 Feasible solution
  • 1.2.4 Optimality criteria
• 1.3 Unconstrained optimization
  • 1.3.1 Univariate functions
  • 1.3.2 Multivariate functions
• 1.4 Nonlinear constrained optimization
  • 1.4.1 Penalty method
  • 1.4.2 Lagrange multipliers
  • 1.4.3 Karush–Kuhn–Tucker conditions
• 1.5 Notes on software

This book introduces the most fundamentals and algor...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• About the author
• Preface
• Acknowledgments
• 1: Introduction to optimization
• 2: Mathematical foundations
• 3: Optimization algorithms
• 4: Data fitting and regression
• 5: Logistic regression, PCA, LDA, and ICA
• 6: Data mining techniques
• 7: Support vector machine and regression
• 8: Neural networks and deep learning
• Bibliography
• Index