EC 524, Winter 2020
Welcome to Economics 524 (424): Prediction and machine-learning in econometrics, taught by Ed Rubin.
Schedule
Lecture Tuesday and Thursday, 10:00am–11:50am, 105 Peterson Hall
Lab Friday, 12:00pm–12:50pm, 102 Peterson Hall
Office hours
- Ed Rubin (PLC 519): Thursday (2pm–3pm); Friday (1pm–2pm)
- Connor Lennon (PLC 430): Monday (1pm-2pm)
Syllabus
Books
Required books
Suggested books
- R for Data Science
- Introduction to Data Science (not available without purchase)
- The Elements of Statistical Learning
Lecture notes
- Why do we have a class on prediction?
- How is prediction (and how are its tools) different from causal inference?
- Motivating examples
001 - Statistical learning foundations
- Why do we have a class on prediction?
- How is prediction (and how are its tools) different from causal inference?
- Motivating examples
Projects
Predicting sales price in housing data (Kaggle)
Lab notes
- General "best practices" for coding
- Working with RStudio
- The pipe (
%>%)
Problem sets
Additional resources
R
- RStudio's recommendations for learning R, plus cheatsheets, books, and tutorials
- YaRrr! The Pirate’s Guide to R (free online)
- UO library resources/workshops
- Eugene R Users
Data Science
- Python Data Science Handbook by Jake VanderPlas
- Elements of AI
- Caltech professor Yaser Abu-Mostafa: Lectures about machine learning on YouTube
- From Google:
Spatial data
- Geocomputation with R (free online)
- Spatial Data Science (free online)
- Applied Spatial Data Analysis with R