Introduction to Interpretable Machine Learning II - Cynthia Rudin

Explore the fundamentals of interpretable machine learning in this comprehensive lecture from the 2022 Program for Women and Mathematics. Delve into topics such as greedy tree induction, information theory, and information gain, with practical examples and training scenarios. Examine modern decision trees, analytical bounds, and their resulting implications. Gain valuable insights from Duke University's Cynthia Rudin as she presents the second part of her introduction to interpretable machine learning, offering a unique perspective on the subject. Engage with thought-provoking questions and participate in a Q&A session to deepen your understanding of this crucial aspect of machine learning.

Introduction
Greedy Tree Induction
Information Theory
Information Gain
Example
Training
Example Cart
Modern Decision Trees
Bounds
Analytical Bounds
Results
Perspective
Questions to think about
Answering questions