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Introduction to Interpretable Machine Learning II - Cynthia Rudin

Introduction to Interpretable Machine Learning II - Cynthia Rudin

Institute for Advanced Study via YouTube Direct link

Introduction

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Introduction

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Classroom Contents

Introduction to Interpretable Machine Learning II - Cynthia Rudin

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  1. 1 Introduction
  2. 2 Greedy Tree Induction
  3. 3 Information Theory
  4. 4 Information Gain
  5. 5 Example
  6. 6 Training
  7. 7 Example Cart
  8. 8 Modern Decision Trees
  9. 9 Bounds
  10. 10 Analytical Bounds
  11. 11 Results
  12. 12 Perspective
  13. 13 Questions to think about
  14. 14 Answering questions

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