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  Multiarmed Bandit Notes, Multiarmed Bandit Slides

