Actuarial Notes

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

By John D. Kelleher, Brian Mac Namee and Aoife D'Arcy · The MIT Press · 2020

Study guide: Exam CFE-201

An introductory textbook that, in the publisher's words, offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with worked examples, and two case studies show the application of these models in the broader business context. The second edition adds a chapter on deep learning and two chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

On the syllabus

  • Exam CFE-201 — Ch. 2 Data to Insights to Decisions (4a, 4b); Ch. 9 Evaluation (4a, 4b); Ch. 12 Case Study: Customer Churn (4a, 4b); Ch. 14 The Art of Machine Learning for Predictive Data Analytics (4a, 4b).

Preface

Notation

List of Figures

List of Tables

Part I Introduction to Machine Learning and Data Analytics

1 Machine Learning for Predictive Data Analytics

  • 1.1 What Is Predictive Data Analytics?
  • 1.2 What Is Machine Learning?
  • 1.3 How Does Machine Learning Work?
  • 1.4 Inductive Bias Versus Sample Bias
  • 1.5 What Can Go Wrong with Machine Learning?
  • 1.6 The Predictive Data Analytics Project Lifecycle: CRISP-DM
  • 1.7 Predictive Data Analytics Tools
  • 1.8 The Road Ahead
  • 1.9 Exercises

2 Data to Insights to Decisions

  • 2.1 Converting Business Problems into Analytics Solutions
    • 2.1.1 Case Study: Motor Insurance Fraud
  • 2.2 Assessing Feasibility
    • 2.2.1 Case Study: Motor Insurance Fraud
  • 2.3 Designing the Analytics Base Table
    • 2.3.1 Case Study: Motor Insurance Fraud
  • 2.4 Designing and Implementing Features
    • 2.4.1 Different Types of Data
    • 2.4.2 Different Types of Features
    • 2.4.3 Handling Time
    • 2.4.4 Legal Issues
    • 2.4.5 Implementing Features
    • 2.4.6 Case Study: Motor Insurance Fraud
  • 2.5 Summary
  • 2.6 Further Reading
  • 2.7 Exercises

3 Data Exploration

  • 3.1 The Data Quality Report
  • 3.2 Getting to Know the Data
  • 3.3 Identifying Data Quality Issues
  • 3.4 Handling Data Quality Issues
  • 3.5 Advanced Data Exploration
  • 3.6 Data Preparation
  • 3.7 Summary
  • 3.8 Further Reading
  • 3.9 Exercises

Part II Predictive Data Analytics

4 Information-Based Learning

  • 4.1 Big Idea
  • 4.2 Fundamentals
  • 4.3 Standard Approach: The ID3 Algorithm
  • 4.4 Extensions and Variations
  • 4.5 Summary
  • 4.6 Further Reading
  • 4.7 Exercises

5 Similarity-Based Learning

  • 5.1 Big Idea
  • 5.2 Fundamentals
  • 5.3 Standard Approach: The Nearest Neighbor Algorithm
  • 5.4 Extensions and Variations
  • 5.5 Summary
  • 5.6 Further Reading
  • 5.7 Epilogue
  • 5.8 Exercises

6 Probability-Based Learning

  • 6.1 Big Idea
  • 6.2 Fundamentals
  • 6.3 Standard Approach: The Naive Bayes Model
  • 6.4 Extensions and Variations
  • 6.5 Summary
  • 6.6 Further Reading
  • 6.7 Exercises

7 Error-Based Learning

  • 7.1 Big Idea
  • 7.2 Fundamentals
  • 7.3 Standard Approach: Multivariable Linear Regression with Gradient Descent
  • 7.4 Extensions and Variations
  • 7.5 Summary
  • 7.6 Further Reading
  • 7.7 Exercises

8 Deep Learning

  • 8.1 Big Idea
  • 8.2 Fundamentals
  • 8.3 Standard Approach: Backpropagation and Gradient Descent
  • 8.4 Extensions and Variations
  • 8.5 Summary
  • 8.6 Further Reading
  • 8.7 Exercises

9 Evaluation

  • 9.1 Big Idea
  • 9.2 Fundamentals
  • 9.3 Standard Approach: Misclassification Rate on a Hold-Out Test Set
  • 9.4 Extensions and Variations
    • 9.4.1 Designing Evaluation Experiments
    • 9.4.2 Performance Measures: Categorical Targets
    • 9.4.3 Performance Measures: Prediction Scores
    • 9.4.4 Performance Measures: Multinomial Targets
    • 9.4.5 Performance Measures: Continuous Targets
    • 9.4.6 Evaluating Models after Deployment
  • 9.5 Summary
  • 9.6 Further Reading
  • 9.7 Exercises

Part III Beyond Prediction

10 Beyond Prediction: Unsupervised Learning

  • 10.1 Big Idea
  • 10.2 Fundamentals
  • 10.3 Standard Approach: The k-Means Clustering Algorithm
  • 10.4 Extensions and Variations
  • 10.5 Summary
  • 10.6 Further Reading
  • 10.7 Exercises

11 Beyond Prediction: Reinforcement Learning

  • 11.1 Big Idea
  • 11.2 Fundamentals
  • 11.3 Standard Approach: Q-Learning, Off-Policy Temporal-Difference Learning
  • 11.4 Extensions and Variations
  • 11.5 Summary
  • 11.6 Further Reading
  • 11.7 Exercises

Part IV Case Studies and Conclusions

12 Case Study: Customer Churn

  • 12.1 Business Understanding
  • 12.2 Data Understanding
  • 12.3 Data Preparation
  • 12.4 Modeling
  • 12.5 Evaluation
  • 12.6 Deployment

13 Case Study: Galaxy Classification

  • 13.1 Business Understanding
  • 13.2 Data Understanding
  • 13.3 Data Preparation
  • 13.4 Modeling
  • 13.5 Evaluation
  • 13.6 Deployment

14 The Art of Machine Learning for Predictive Data Analytics

  • 14.1 Different Perspectives on Prediction Models
  • 14.2 Choosing a Machine Learning Approach
    • 14.2.1 Matching Machine Learning Approaches to Projects
    • 14.2.2 Matching Machine Learning Approaches to Data
  • 14.3 Beyond Prediction
  • 14.4 Your Next Steps

Part V Appendices

Appendix A Descriptive Statistics and Data Visualization for Machine Learning

Appendix B Introduction to Probability for Machine Learning

Appendix C Differentiation Techniques for Machine Learning

Appendix D Introduction to Linear Algebra

Bibliography

Index

Sources