Sale!

Optimization Techniques in Artificial Intelligence: Foundations, Mathematical Models, Algorithms, and Real-World Applications (Vol-1)

Optimization Techniques in Artificial Intelligence: Foundations, Mathematical Models, Algorithms, and Real-World Applications (Vol-1) is a comprehensive textbook that introduces the mathematical foundations and practical optimization techniques powering modern Artificial Intelligence. Covering linear programming, convex optimization, gradient descent, optimization algorithms, machine learning, and deep learning, this book provides step-by-step explanations, real-world examples, illustrations, and AI applications. It is an ideal resource for undergraduate, postgraduate, researchers, AI professionals, data scientists, and educators seeking a strong foundation in optimization methods used in intelligent systems.

Category: mathematics with ai

Tags: Academic AI Book, AdaGrad, Adam Optimizer, AdamW, AI Algorithms, AI Engineering, AI Mathematics, AI Optimization, AI Reference Book, AI Research, Applied Artificial Intelligence., Artificial Intelligence Book, Artificial Intelligence Education, Artificial Intelligence Textbook, Computational Intelligence, Computer Science Book, Constraints Optimization, Convex Functions, Convex Optimization, Convex Sets, Coordinate Descent, Cross Entropy Loss, Data Analytics, Data Science Book, Deep Learning Book, Deep Learning Optimization, Duality Theory, Elastic Net, Gradient Descent, Graduate AI Textbook, Intelligent Systems, Interior Point Method, KKT Conditions, L1 Regularization, L2 Regularization, Lagrangian Optimization, Linear Algebra for AI, Linear Programming, Linear Regression, Logistic Regression, Loss Functions, Machine Learning Book, Machine Learning Optimization, Mathematical Optimization, Matrix Calculus, Mean Squared Error, Mini Batch Gradient Descent, Momentum Optimization, Nesterov Gradient, Neural Network Optimization, Newton Method, Objective Function, Operations Research, Optimization Algorithms, Optimization for Deep Learning, Optimization in Machine Learning, Optimization Techniques in Artificial Intelligence, Optimization Theory, Probability for AI, Professional AI Guide, Regularization, RMSProp, Sensitivity Analysis, Simplex Method, Sparse Optimization, Stochastic Gradient Descent, Support Vector Machines, University AI Book

Description
Reviews (0)

Description

Optimization lies at the heart of modern Artificial Intelligence. Every intelligent system—from machine learning models and deep neural networks to robotics, recommendation systems, and autonomous agents—depends on optimization techniques to achieve the best possible performance.

Optimization Techniques in Artificial Intelligence (Vol-1) is a comprehensive and practical guide designed to help students, researchers, educators, and AI professionals understand the mathematical principles and computational algorithms that drive intelligent decision-making.

Starting from the fundamental concepts of optimization, the book gradually introduces linear programming, simplex methods, convex optimization, gradient-based algorithms, and optimization techniques used in deep learning. Every chapter combines mathematical theory with practical AI applications, enabling readers to bridge the gap between academic concepts and real-world implementation.

This volume emphasizes conceptual clarity, mathematical rigor, and practical relevance, making it suitable both as a university textbook and as a professional reference.

What You Will Learn

✔ Foundations of optimization in Artificial Intelligence

✔ Mathematical tools for optimization

✔ Linear algebra and optimization fundamentals

✔ Linear Programming and optimization modeling

✔ Simplex and Interior Point algorithms

✔ Duality Theory and Sensitivity Analysis

✔ Convex Optimization principles

✔ Convex optimization algorithms

✔ Gradient Descent methods

✔ Stochastic Gradient Descent (SGD)

✔ Mini-Batch Gradient Descent

✔ Momentum Optimization

✔ Nesterov Accelerated Gradient

✔ AdaGrad

✔ RMSProp

✔ Adam Optimizer

✔ AdamW

✔ Optimization landscapes

✔ Learning rate optimization

✔ Loss functions in Machine Learning

✔ Regularization techniques

✔ Optimization in Deep Learning

✔ Practical AI optimization case studies

Key Features

• Comprehensive coverage of AI optimization fundamentals

• Easy-to-understand mathematical explanations

• Step-by-step algorithms and derivations

• Numerous diagrams and conceptual illustrations

• Practical AI examples and real-world applications

• Machine Learning optimization techniques

• Deep Learning optimization methods

• Convex optimization with intuitive explanations

• Linear Programming applications

• University-level textbook

• Research-oriented content

• Industry-relevant optimization techniques

• Ideal for self-learning and academic courses

Table of Contents Highlights

Part I – Foundations of Optimization and AI

Introduction to Optimization
Optimization in Intelligent Systems
Mathematical Foundations
Linear Algebra
Probability for Optimization
Convex Sets
Optimization Formulation

Part II – Linear Programming

Linear Programming Fundamentals
Objective Functions
Constraints
Simplex Algorithm
Interior Point Methods
Duality Theory
Sensitivity Analysis

Part III – Convex Optimization

Convex Functions
Convex Sets
KKT Conditions
Lagrangian Optimization
Newton Methods
Coordinate Descent
Convex Machine Learning Models
Support Vector Machines
Regularization

Part IV – Gradient-Based Optimization

Gradient Descent
Stochastic Gradient Descent
Mini-Batch Optimization
Momentum
Nesterov
AdaGrad
RMSProp
Adam
AdamW
Loss Functions
Multi-objective Optimization

Who Should Read This Book?

This book is ideal for:

Undergraduate Students
Postgraduate Students
PhD Scholars
AI Researchers
Machine Learning Engineers
Data Scientists
Deep Learning Practitioners
Computer Science Faculty
Software Engineers
AI Professionals
Data Analytics Students
Research Laboratories
University Libraries

Why This Book?

Unlike books that focus only on mathematical theory or only on programming implementation, this volume combines both perspectives. Readers gain a deep understanding of optimization concepts while learning how these methods power modern Artificial Intelligence systems.

Whether you are studying Machine Learning, Deep Learning, Operations Research, Data Science, or Artificial Intelligence, this book provides the essential optimization knowledge required for designing efficient and intelligent algorithms.

Suitable For

B.Tech
BCA
MCA
B.Sc. Computer Science
M.Tech
M.Sc. Computer Science
Artificial Intelligence Courses
Machine Learning Courses
Data Science Programs
Research Projects
Competitive Exam Preparation
Academic Reference
Professional Learning

Book Details

Title: Optimization Techniques in Artificial Intelligence

Subtitle: Foundations, Mathematical Models, Algorithms, and Real-World Applications (Vol-1)

Language: English

Subject: Artificial Intelligence, Machine Learning, Mathematical Optimization

Level: Beginner to Advanced

Category: AI | Machine Learning | Data Science | Optimization | Computer Science

Reviews

There are no reviews yet.

Be the first to review “Optimization Techniques in Artificial Intelligence: Foundations, Mathematical Models, Algorithms, and Real-World Applications (Vol-1)”