Welcome to Economics 524 (424): Prediction and machine-learning in econometrics, taught by Ed Rubin and Andrew Dickinson.

Lecture Monday and Wednesday, 10:00a–11:20a (Pacific), 220 Chapman

Lab Friday, 12:00p–12:50p (Pacific), 220 Chapman

Office hours

• Ed Rubin Tuesdays, 3:30p–5:00p, Zoom
• Andrew Dickinson Thursdays, 2:00p-3:00p, Zoom

Syllabus

• Introduction to Statistical Learning
• The Hundred-Page Machine Learning Book
• Data Visualization

• R for Data Science
• Introduction to Data Science (not available without purchase)
• The Elements of Statistical Learning
• Data Science for Public Policy (not available without purchase)

000 - Overview (Why predict?)

1. Why do we have a class on prediction?
2. How is prediction (and how are its tools) different from causal inference?
3. Motivating examples

Formats .html | .pdf | .Rmd

Readings Introduction in ISL

001 - Statistical learning foundations

1. Why do we have a class on prediction?
2. How is prediction (and how are its tools) different from causal inference?
3. Motivating examples

Formats .html | .pdf | .Rmd

Readings

• Prediction Policy Problems by Kleinberg et al. (2015)
• ISL Ch1
• ISL Start Ch2

Supplements Unsupervised character recognization

002 - Model accuracy

1. Model accuracy
2. Loss for regression and classification
3. The variance-bias tradeoff
4. The Bayes classifier
5. KNN

Formats .html | .pdf | .Rmd

Readings

• ISL Ch2–Ch3
• Optional: 100ML Preface and Ch1–Ch4

003 - Resampling methods

1. Review
2. The validation-set approach
3. Leave-out-out cross validation
4. k-fold cross validation
5. The bootstrap

Formats .html | .pdf | .Rmd

Readings

• ISL Ch5
• Optional: 100ML Ch5

004 - Linear regression strikes back

1. Returning to linear regression
2. Model performance and overfit
3. Model selection—best subset and stepwise
4. Selection criteria

Formats .html | .pdf | .Rmd

Readings

• ISL Ch3
• ISL Ch6.1

In between: tidymodels-ing

• An introduction to preprocessing with tidymodels. (Kaggle notebook)
• An introduction to modeling with tidymodels. (Kaggle notebook)
• An introduction to resampling, model tuning, and workflows with tidymodels (Kaggle notebook)
• Introduction to tidymodels: Follow up for Kaggle

005 - Shrinkage methods

(AKA: Penalized or regularized regression)

1. Ridge regression
2. Lasso
3. Elasticnet

Formats .html | .pdf | .Rmd

Readings

• ISL Ch4
• ISL Ch6

006 - Classification intro

1. Introduction to classification
2. Why not regression?
3. But also: Logistic regression
4. Assessment: Confusion matrix, assessment criteria, ROC, and AUC

Formats .html | .pdf | .Rmd

Readings

• ISL Ch4

007 - Decision trees

1. Introduction to trees
2. Regression trees
3. Classification trees—including the Gini index, entropy, and error rate

Formats .html | .pdf | .Rmd

Readings

• ISL Ch8.1–Ch8.2

008 - Ensemble methods

1. Introduction
2. Bagging
3. Random forests
4. Boosting

Formats .html | .pdf | .Rmd

Readings

• ISL Ch8.2

009 - Support vector machines

1. Hyperplanes and classification
2. The maximal margin hyperplane/classifier
3. The support vector classifier
4. Support vector machines

Formats .html | .pdf | .Rmd

Readings

• ISL Ch9

010 - Dimensionality reduction and unsupervised learning

0. MNIST dataset (machines with vision)
1. K-means clustering
2. Principal component analysis (PCA)
3. UMAP

Formats .html | .pdf | .Rmd

Planned projects

000 Predicting sales price in housing data (Kaggle)

Help:

• A simple example/walkthrough
• Kaggle notebooks (from Connor Lennon)

001 Validation and out-of-sample performance

002 Penalized regression, logistic regression, and classification

003 Nonlinear predictors

004 Image/multi-class classification (MNIST)

Outline of the project

Topic and group due by February 16th.

Final project submission due by midnight on March 9th.

Approximate/planned topics...

000 - Workflow and cleaning

1. General "best practices" for coding
2. Working with RStudio
3. The pipe (%>%)
4. Cleaning and Kaggle follow up

Formats .html | .pdf | .Rmd

001 - Workflow and cleaning (continued)

1. Finish previous lab on dplyr
2. Working with projects
3. Using dpylr and ggplot2 to make insightful visuals
4. How to fix a coding error

Housing data download

Formats .html | .Rmd

002 - Validation

1. Creating a training and validation data set from your observations dataframe in R
2. Writing a function to iterate over multiple models to test and compare MSEs

Formats .html | .Rmd

003 - Practice using tidymodels

1. Cleaning data quickly and efficiently with tidymodels

Formats .html

004 - Practice using tidymodels (continued)

1. An introduction to preprocessing with tidymodels (refresher from last week)
2. An introduction to modeling with tidymodels
3. An introduction to resampling, model tuning, and workflows with tidymodels (will finish up next week)

005 - Summarizing tidymodels

1. Summarizing tidymodels
2. Combining pre-split data together and then defining a custom split

006 - Penalized regression in tidymodels + functions + loops

1. Running a Ridge, Lasso or Elasticnet logistic regression in tidymodels.
2. A short lesson in writing functions and loops in R)

007 - Finalizing a workflow in tidymodels: Example using a random forest

1. Finalizing a workflow in tidymodels: Example using a random forest
2. A short lesson in writing functions and loops in R (continued)

• 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:
  • Machine-learning crash course
  • Google Cloud training for data and machine learning
  • General Google education platform

• Geocomputation with R (free online)
• Spatial Data Science (free online)
• Applied Spatial Data Analysis with R