Introduction to Machine Learning Algorithms

Basic Principles and Mathematics

Vinod Kumar Khanna

• Publisher: Chapman and Hall/CRC
• Year: 2026
• Topic: Computer Science
• eBook ISBN:
  9781040638774
• Subtopic: Computer Engineering
• Index: Computer Science

Mathematics is the foundation of machine learning algorithms. To understand the shortcomings of existing algorithms and develop more effective methods, it is essential to understand the mathematical concepts underlying these algorithms and their operational principles. This book serves as an introductory resource, outlining the preliminary concepts and offering insights into the mathematical foundations and operational mechanisms of machine learning algorithms. It describes the basic equations and interrelates the questions arising during practical applications of machine learning with the basic mathematical picture of the algorithms used.

Features

• Introduces machine learning, highlights the central role of algorithms in machine learning, and explains the core mathematical prerequisites to understanding machine learning algorithms

• Systematically examines the sequential steps of classical machine learning algorithms used for classification of data sets into distinct groups; regression, clustering analysis,

• Provides an overview of value, policy, and model-based reinforcement learning algorithms.

This book is for academicians, scholars, students, and professionals engaged in the study of machine learning and artificial intelligence.

Table of contents

• Cover Page
• Half-Title Page
• Title Page
• Copyright Page
• Dedication Page
• Contents
• Acronyms, Abbreviations, and Symbols
• Mathematical Notation
• Greek Letters and Other Symbols
• Preface
• Acknowledgments
• About the Author
• About the Book
• Chapter 1 Algorithms in Everyday Life and Computer Science
• Chapter 2 Algorithmic Foundations of Machine and Deep Learning
• Chapter 3 Classification Algorithms: Logistic Regression
• Chapter 4 Decision Trees and Random Forest Classifiers
• Chapter 5 Support Vector Machines, K-Nearest Neighbors, and Naive Bayes’ Classifier Algorithms
• Chapter 6 Regression Analysis: Linear, Multiple Linear, and Nonlinear Regression
• Chapter 7 Lasso, Ridge, and Support Vector Regression
• Chapter 8 Miscellaneous Regression Algorithms: Decision Tree, Random Forest, KNN Regression, and Others
• Chapter 9 Clustering Algorithms: Centroid-Based, Density-Based, Distribution-Based, and Hierarchical
• Chapter 10 Affinity Propagation, Fuzzy Clustering, and OPTICS
• Chapter 11 Feature Selection Algorithms: Filter, Wrapper, and Embedded Methods
• Chapter 12 Feature Extraction Algorithms: Principal Component Analysis and Linear Discriminant Analysis
• Chapter 13 Feedforward Neural Networks and Self-Organizing Maps
• Chapter 14 Perceptron, Multilayer Perceptron, and Radial Basis Function Networks
• Chapter 15 Convolutional Neural Networks
• Chapter 16 Recurrent, Long Short-Term Memory and Transformer Networks
• Chapter 17 Restricted Boltzmann Machine and Deep Belief Network
• Chapter 18 Generative Adversarial Networks
• Chapter 19 Autoencoders
• Chapter 20 Modular Neural Networks
• Chapter 21 Value-, Policy-, and Model-Based Reinforcement Learning Algorithms
• Glossary A: Key Terminology of Machine Learning Algorithms
• Glossary B: Brief Rundown of Machine Learning Algorithms and Related Methods
• Index