Welcome to Economics 524 (424): Prediction and machine-learning in econometrics, taught by Ed Rubin.
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)
- R for Data Science
- Introduction to Data Science (not available without purchase)
- The Elements of Statistical Learning
- 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
- Model accuracy
- Loss for regression and classification
- The variance bias-tradeoff
- The Bayes classifier
- KNN
- Review
- Hold-out methods
- The validation-set approach
- Cross validation
- The bootstrap
In-class: Validation-set exercise (Kaggle)
Intro Predicting sales price in housing data (Kaggle)
001 KNN and loss (Kaggle notebook)
You will need to sign into you Kaggle account and then hit "Copy and Edit" to add the notebook to your account.
Due 21 January 2020 before midnight.
- General "best practices" for coding
- Working with RStudio
- The pipe (
%>%)
dplyr and Kaggle notebooks
- 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
- Python Data Science Handbook by Jake VanderPlas
- Elements of AI
- Caltech professor Yaser Abu-Mostafa: Lectures about machine learning on YouTube
- From Google:
- Geocomputation with R (free online)
- Spatial Data Science (free online)
- Applied Spatial Data Analysis with R