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Machine Learning for Public Policy (Spring 2016) rayid
CAPP 30524 – Machine Learning for Public Policy
Office: Searle 219 (5735 S Ellis)
Office Hours: Tuesday and Thursdays 12-1pm (or by appointment)
Email: rayid [at] uchicago [dot] edu
TA: Gustav Larsson
Email: larsson [at] cs [dot] uchicago [dot] edu
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 for social impact and policy problems.
This course will teach students about:
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
This is a hands-on course where students will be expected to use Python (as well as other computational tools) to implement solutions to various policy problems. We will cover supervised and unsupervised learning algorithms and will learn 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. There will be a project that students will do in teams of 3-4.
Two courses in Computer Programming (Python experience required),
Two courses in Probability & Statistics.
Prior experience with data analysis is highly recommended (using SQL, R, Python)
Machine Learning Process
Map to Machine Learning formulation
Understand the Data
Data Prep and Initial Analysis
Machine Learning Methods
Semi-Supervised (Not covered in this class)
Applying these methods to Policy Problems
The following lectures are a work in progress. The schedule is subject to change based on class interest and progress. In addition, we will have guest lectures which will cause some of these lectures to be merged. If there are additional topics you’d like to cover or guest lectures you’d like to see, please let me know.
If you’re trying to reuse the slides below, most of the lecture is done on the white/chalk board and the ppt presentations are not very detailed.
Course Overview and Introduction: Goals, Expectations, Structure [slides]
Case Studies: Machine Learning used in Public Policy problems
Machine Learning Process and Workflow/Pipeline Overview
Project Proposal Presentations
Machine Learning Methods: Unsupervised Learning
Machine Learning Methods: Supervised Learning I
Machine Learning Methods: Supervised Learning II
Machine Learning Methods: Supervised Learning III
Evaluation Methodology I – Offline Evaluation
Evaluation Methodology II – Experiments
Mapping Policy Problems to Machine Learning Problems
Open Source and Commercial Machine Learning Tools Overview
Putting it all together: Case Study I
Putting it all together: Case Study II
Advanced Topics: Reinforcement Learning, Active Learning
Ethics, Privacy, Transparency
Recap and Class Discussion
Short response to previous week’s lectures due Tuesday before class
Project: Students will form groups (3-4 students each) and work on a project they’ll propose after week 2