Introduction to Interpretable Machine Learning - Cynthia Rudin

This course provides an introduction to interpretable machine learning, focusing on topics such as machine learning, vectors, classification, natural language processing, model complexity, decision trees, data set, and information theory. The course aims to teach learners the fundamentals of interpretable machine learning and how to apply these concepts in practice. The teaching method includes lectures and demonstrations. The course is designed for individuals interested in understanding the mathematical aspects of machine learning, particularly women in mathematics looking to explore the intersection of mathematics and machine learning.