Introduction to Artificial Intelligence and Machine Learning, with eBook Access Code

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.

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