Intuition for the Algorithms of Machine Learning

A Multimedia Textbook by Cynthia Rudin

YouTubeVideos for all lectures are available at this playlist.

Chapter 1.1 Concepts of Learning Notes, Ockham's Razor Basics Slides
Chapter 1.2 ROC Curves Slides, Part I, ROC Curves Slides, Part II, ROC Curves Notes, ROC Curves Exploratory Data Analysis
Chapter 1.3 Cross Validation Slides, Cross Validation Notes
Chapter 2.1 Decision Trees Notes, Decision Trees Slides
Chapter 2.2 Modern Decision Trees Notes, GOSDT Computation Example Walkthrough by Chudi Zhong
Chapter 3.1 Random Forest Slides, Random Forest Notes
Chapter 3.2 Variable Importance Notes
Chapter 3.3 Boosting Notes, Boosting Slides
Chapter 3.4 Interpretable Generalized Additive Models via Boosting
Chapter 4 Logistic Regression
Chapter 5.1 Convex Optimization
Chapter 5.2 Support Vector Machines
Chapter 5.3 Kernels Notes, Kernels Slides
Chapter 6 Statistical Learning Theory Notes, Statistical Learning Theory Slides
Chapter 7 Least Squares and Friends Notes
Chapter 8 Dimension Reduction for Data Visualization
Chapter 9 Perceptron and Winnow Notes
Chapter 10 Clustering Notes, Clustering Slides
Chapter 11 Gaussian Mixture Models and Expectation Maximization
Chapter 12.1 Neural Networks Slides
Chapter 12.2 Cross Entropy is Logistic Loss
Chapter 13 Multi-armed Bandit Notes, Multi-armed Bandit Slides