This repo has all the work done for Machine Learning for Public Policy, taught by Rayid Ghani (http://www.rayidghani.com/). Rayid is a Research Director at the Computation Institute, Senior Fellow at the Harris School of Public Policy, and Director of the Center for Data Science and Public Policy at the University of Chicago.
This course will be an introduction to machine learning and how it can be applied to public policy problems. It’s designed for students who are interested in learning how to use modern, scalable, computational data analysis methods and tools, and applying them to social and policy problems.
Goals of the course:
- What role Machine Learning can play in designing, implementing, evaluating, and improving Public Policy.
- Machine Learning methods and tools.
- How to solve policy problems using machine learning methods and tools.
- Cover supervised and unsupervised learning algorithms and how to use them with data from a variety of public policy problems in areas such as education, public health, sustainability, economic development, and public safety.
Machine Learning Process
- Data Prep and Initial Analysis
- Feature Development
- Modeling
- Evaluation
- Deployment
Machine Learning Methods
- Unsupervised
- Clustering
- PCA
- Supervised
- Regression
- KNN
- Trees
- NN
- SVM
- Random Forests
- Ensemble Methods