Introduction to Interpretable Machine Learning II - Cynthia Rudin

This course aims to teach learners about the principles and techniques of interpretable machine learning. The learning outcomes include understanding greedy tree induction, information theory, information gain, modern decision trees, analytical bounds, and gaining a perspective on the topic. The course covers individual skills such as building decision trees, calculating information gain, and interpreting machine learning results. The teaching method involves lectures, examples, and discussions. The intended audience for this course is individuals interested in machine learning, particularly those looking to understand and interpret machine learning models.