Teaching Materials for Machine Learning

Here you will find teaching materials from sections I ran as part of Harvard Kennedy School's Machine Learning and Big Data Analytics class (Fall 2018). The main goal of these sessions was to enable students to use R for machine learning, though the sessions also included brief and high-level conceptual reviews (the PDF documents).

Getting Started with R

The R code is intended to be used in RStudio, which can be downloaded here. You will also need to install R, which you can do here. Please download and install both R and RStudio before attempting to run the code included on this page.

Conceptual Introductions to Topics in Machine Learning

  1. Introduction to Machine Learning and k-Nearest Neighbors (k-NN)

  2. k-Nearest Neighbors (k-NN) and Linear Regression

  3. Logistic Regression

  4. Cross-validation and Linear / Quadratic Discriminant Analysis (LDA and QDA)

  5. Bootstrapping and Penalized Regressions (Lasso and Ridge Regression)

  6. Principal Components Analysis (PCA) and Partial Least Squares (PLS)

  7. Random Forest

  8. Support Vector Machines (SVM)

  9. Reinforcement Learning and Clustering

R Code for Machine Learning

  1. Introduction to Machine Learning and k-Nearest Neighbors (k-NN)

  2. Linear Regression

  3. Logistic Regression

  4. Cross-validation and Linear / Quadratic Discriminant Analysis (LDA and QDA)

  5. Bootstrapping and Penalized Regressions (Lasso and Ridge Regression)

  6. Principal Components Analysis (PCA) and Partial Least Squares (PLS)

  7. Random Forest, Splines, and Generalized Additive Models (GAMs)

  8. Support Vector Machines (SVM)

  9. Reinforcement Learning and Clustering