Helps students unlock the power of AI and Machine Learning to achieve business success and future-proof their careers
Artificial intelligence and machine learning are transforming the modern workplace, making AI literacy a critical skill for business professionals. Introduction to Artificial Intelligence and Machine Learning equips students with essential AI/ML knowledge and practical skills, enabling them to leverage cutting-edge technology in today’s data-driven world.
With an engaging and accessible approach, this textbook ensures that students—regardless of technical background—gain a working knowledge of AI/ML systems. Concise, easy-to-digest chapters blend foundational concepts with real-world applications to help students develop the expertise needed to implement AI/ML solutions across industries.
For instructors, the textbook offers flexible teaching methodologies, whether focusing on conceptual discussions, light technology applications, or full AI/ML projects. With a clear business perspective and a strong emphasis on AI governance and deployment, the textbook prepares students to navigate the future of AI in the workplace with confidence.
Helping students build a solid foundation in key concepts while exploring strategic implementation and ethical considerations, Introduction to Artificial Intelligence and Machine Learning is ideal for undergraduate and graduate students in business, engineering, and healthcare programs taking courses such as Business Analytics, Information Systems, and AI Strategy.
WILEY ADVANTAGE
- Provides an introduction to artificial intelligence and machine learning designed to make complex concepts understandable
- Prepares students for AI-driven careers by aligning learning objectives with employer demand for AI/ML skills
- Explains AI/ML model development, deployment, and maintenance with clear step-by-step guidance
- Integrates real-world business applications and case studies to demonstrate AI/ML’s impact across industries
- Discusses governance in AI/ML to facilitate responsible implementation and decision-making
- Includes practical coding exercises and in-class projects to build essential AI/ML skills for the workforce
- Features a robust suite of instructor resources, including an extensive Instructor’s Manual, Test Bank, and PowerPoint slides
AN INTERACTIVE, MULTIMEDIA LEARNING EXPERIENCE
This textbook includes access to an interactive, multimedia e-text. Icons throughout the print book signal corresponding digital content in the e-text.
Video Clips created by the author complement the text and engage students more deeply with AI/ML concepts and applications.
Interactive Questions appear in each chapter of the enhanced e-text, providing students with immediate feedback to strengthen learning.
Table of Contents:
Preface vii
1 Artificial Intelligence and Machine Learning and You 1
4.2 Characteristics of Problems Suitable for AI/ML Solutions 93
4.3 The AI/ML Deployment Process 96
4.4 What’s in AI/ML for Me? 104
1.1 The Modern Business Environment 4
1.2 A Brief History of Artificial Intelligence and Machine Learning 6
1.3 Definitions 8
1.4 Why You Should Learn About AI and ml 11
1.5 Organizational Roles in AI/ML Projects 13
1.6 What’s in AI/ML for Me? 19
2 Fundamentals of Artificial Intelligence and Machine Learning 31
Introduction 33
2.1 Conventional Programming Versus AI/ML 33
2.2 The Basics of AI/ML Systems 38
2.3 Advantages of AI/ML Systems 40
2.4 Disadvantages of AI/ML Systems 42
2.5 What’s in AI/ML for Me? 48
3 Strategic Considerations for AI/ML Development 54
3.1 AI/ML Maturity Levels for Organizations 56
3.2 Align AI/ML Projects with Organizational Strategy 59
3.3 Major Strategic Models for AI/ML Implementation 62
3.4 Link Model Metrics to Organizational KPIs 68
3.5 Change Management in AI/ML Adoption 70
3.6 AI/ML Governance 73
3.7 What’s in AI/ML for Me? 77
4 The Business Problem 84
Introduction 85
4.1 Understand and Define the Business Problem 86
5 Data Management 108
Introduction 110
5.1 Fundamentals of Data 110
5.2 Data Sources 112
5.3 Feature Engineering 116
5.4 Data Cleaning and Preprocessing 120
5.5 Select Independent Variables and Dependent Variables and Split the Data 124
5.6 What’s in AI/ML for Me? 127
6 AI/ML Model Training 135
Introduction 135
6.1 Supervised Machine Learning Algorithms: Regression 136
6.2 Supervised Machine Learning Algorithms: Classification 139
6.3 Unsupervised Machine Learning Algorithms 159
6.4 Challenges in Model Training 161
6.5 Strategies for Model Improvement 165
6.6 What’s in AI/ML for Me? 167
7 Neural Networks and Monitoring and Maintaining AI/ML Models 177
7.1 Introduction to Neural Networks 177
7.2 AI/ML Model Monitoring 186
7.3 AI/ML Model Maintenance 191
7.4 What’s in AI/ML for Me? 194
8 Generative Machine Learning (Generative AI) 200
Introduction 201
8.1 Foundation Models 202
8.2 Introduction to Generative AI and Its Business Applications 207
8.3 Limitations of Generative AI Models 213
8.4 Prompt Engineering 219
8.5 What’s in AI/ML for Me? 223
Appendix 229
Index 307
About the Author :
R. Kelly Rainer is the George Phillips Privet Professor in the Department of Business Analytics and Information Systems at Auburn University. He has published in leading journals such as MIS Quarterly, Journal of Management Information Systems, and Decision Sciences. A recognized expert in information systems and business analytics, Rainer is a member of the Decision Sciences Institute and the Association for Information Systems.
