Machine Learning Foundations, Volume 1: Supervised Learning [Paperback]

Roi Yehoshua is a professor in the Department of Electrical and Computer Engineering at Northeastern University, where he develops and teaches graduate courses in machine learning and data science. With over two decades of experience spanning academia and industry, he has developed and taught a wide range of machine learning courses, including pioneering the university's first course on Large Language Models. His writing on machine learning has reached over 200,000 readers worldwide through platforms like Medium and Towards Data Science.

Preface xv
About the Author xxvii

Chapter 1: Introduction to Machine Learning 1
1.1 Formal Definition 1
1.2 Types of Machine Learning 3
1.3 Related Fields 4
1.4 Brief History 5
1.5 Machine Learning Applications 8
1.6 Limitations of Machine Learning 9
1.7 Ethical Considerations 10
1.8 Software Libraries 11
1.9 Common Datasets 12
1.10 Current Trends and Future Directions 14
1.11 Summary 14

Chapter 2: Supervised Machine Learning 15
2.1 Formal Definition 16
2.2 Machine Learning Models 19
2.3 The Data-Generating Process 24
2.4 Generalization Error and Empirical Risk Minimization 28
2.5 Parameter Estimation 29
2.6 The BiasVariance Tradeoff 31
2.7 Building a Machine Learning Model 36
2.8 Challenges in Supervised Learning 39
2.9 Summary 40
2.10 Exercises 41

Chapter 3: Introduction to Scikit-Learn 49
3.1 Main Features 50
3.2 Installation 50
3.3 The Estimator API 51
3.4 Typical Workflow of Building a Model 56
3.5 Example: Iris Classification 56
3.6 Pipelinel(