Author Profile

Pierre Laurent is an internationally acclaimed computer scientist, author, and researcher dedicated to unlocking the practical potential of machine intelligence. Driven by a passion for creating safe, accessible, and highly optimized digital solutions, he has positioned himself at the absolute forefront of the current AI revolution. In Handbook of Artificial Intelligence, Laurent distills decades of complex operational research, industry case studies, and advanced machine learning methodologies into a comprehensive guide designed for both pioneering developers and business strategists. Over his illustrious career, he has advised tech conglomerates, spearheaded cross-border cognitive computing research projects, and mentored the next wave of engineers. When he isn't developing architectural paradigms for enterprise-level neural networks, Pierre frequently keynotes global technology summits, speaking passionately about the intersection of data frameworks, computational scaling, and the societal shifts driving modern technology.

About The Book

Artificial Intelligence (AI) is an interdisciplinary science with multiple approaches to solve a problem. Advancements in machine learning (ML) and deep learning are creating a paradigm shift in virtually every tech industry sector. This handbook provides a quick introduction to concepts in AI and ML. The sequence of the book contents has been set in a way to make it easy for students and teachers to understand relevant concepts with a practical orientation. This book starts with AI/ML and its applications. Subsequent chapters cover predictions using ML, and focused information about AI/ML algorithms for different industries (health care, agriculture, autonomous driving, image classification and segmentation, SEO, smart gadgets and security). Each industry usecase demonstrates a specific aspect of AI/ML techniques that can be used to create pipelines for technical solutions such as data processing, object detection, classification and more.Designed as a multi-layered asset, this book transitions effortlessly from fundamental algorithms to complex, sector-specific production pipelines. It equips students, computer scientists, and technology leaders with both the theoretical intuition and the tactical code patterns required to build, scale, and govern intelligent systems.

Table of Contents

Preface
Chapter 1. Foundations of Artificial Intelligence
Historical Evolution of Artificial Intelligence
Definitions and Scope of Artificial Intelligence
Branches and Subfields of AI
Intelligent Agents and Environments
Problem-Solving Paradigms
Knowledge Representation Fundamentals
Search Strategies and State Space Models
Ethical and Societal Implications of AI
Chapter 2. Mathematics and Statistics for AI
Linear Algebra in Machine Learning
Probability Theory and Random Variables
Statistical Inference and Estimation
Optimization Techniques
Calculus for Machine Learning
Information Theory
Graph Theory and Networks
Numerical Methods and Computational Complexity
Chapter 3. Machine Learning Fundamentals
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Model Evaluation and Validation
Bias–Variance Tradeoff
Overfitting and Regularization
Feature Engineering and Selection
Ensemble Learning Methods
Chapter 4. Deep Learning Architectures
Artificial Neural NetworksBackpropagation Algorithm
Convolutional Neural Networks
Recurrent Neural Networks
Long Short-Term Memory Networks
Transformer Models
Generative Adversarial Networks
Model Optimization and Deployment
Chapter 5. Natural Language Processing
Text Preprocessing Techniques
Language Modeling
Word Embeddings
Sequence-to-Sequence Models
Machine Translation
Sentiment Analysis
Question Answering Systems
Large Language Models
Chapter 6. Computer Vision and Pattern Recognition
Image Processing Fundamentals
Feature Extraction Techniques
Object Detection and Recognition
Image Segmentation
Face Recognition Systems
Video Analytics
Multimodal Learning
Applications in Healthcare and Security
Chapter 7. Robotics and Intelligent Systems
Fundamentals of Robotics
Perception and Sensor Integration
Motion Planning and Control
Autonomous Navigation
Human–Robot Interaction
Swarm Intelligence
Industrial and Service Robotics
Ethical Issues in Robotics
Chapter 8. Knowledge-Based and Expert Systems
Rule-Based Systems
Fuzzy Logic Systems
Ontologies and Semantic Web
Case-Based Reasoning
Decision Support Systems
Knowledge Graphs
Explainable AI
Applications in Industry
Chapter 9. AI Infrastructure and Engineering
Data Engineering and Pipelines
Big Data Technologies
Cloud Computing for AI
Distributed Machine Learning
Model Deployment and MLOps
AI Hardware Accelerators
Security and Privacy in AI Systems
Performance Monitoring and Maintenance
Chapter 10. AI Applications and Future Directions
AI in Healthcare
AI in Finance
AI in Education
AI in Agriculture
AI in Smart Cities
AI for Climate and Sustainability
Policy and Governance of AI
Emerging Trends and Research Frontiers
Bibliography
Index