{"id":24099772,"url":"https://github.com/edrubin/ec524w25","last_synced_at":"2026-02-22T18:36:54.391Z","repository":{"id":271281443,"uuid":"912945770","full_name":"edrubin/EC524W25","owner":"edrubin","description":"Masters-level applied econometrics course—focusing on prediction—at the University of Oregon (EC424/524 during Winter quarter, 2025). 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I will update links to the new slides as we work our way through the term/slides.\n\n[**000 - Overview (Why predict?)**](https://raw.githack.com/edrubin/EC524W25/master/lecture/000/slides.html)\n\n1. Why do we have a class on prediction?\n2. How is prediction (and how are its tools) different from causal inference?\n3. Motivating examples\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/master/lecture/000/slides.html) | [.pdf](https://github.com/edrubin/EC524W25/blob/master/lecture/000/slides.pdf) | [.rmd](https://github.com/edrubin/EC524W25/blob/master/lecture/000/slides.rmd)\n\n**Readings** Introduction in *ISL*\n\n[**001 - Statistical learning foundations**](https://raw.githack.com/edrubin/EC524W25/master/lecture/001/slides.html)\n\n1. Why do we have a class on prediction?\n2. How is prediction (and how are its tools) different from causal inference?\n3. Motivating examples\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/master/lecture/001/slides.html) | [.pdf](https://github.com/edrubin/EC524W25/blob/master/lecture/001/slides.pdf) | [.rmd](https://github.com/edrubin/EC524W25/blob/master/lecture/001/slides.rmd)\n\n**Readings**\n\n- [Prediction Policy Problems](https://www.aeaweb.org/articles?id=10.1257/aer.p20151023) by Kleinberg *et al.* (2015)\n- *ISL* Ch1\n- *ISL* Start Ch2\n\n**Supplements** [Unsupervised character recognization](https://colah.github.io/posts/2014-10-Visualizing-MNIST/)\n\n[**002 - Model accuracy**](https://raw.githack.com/edrubin/EC524W25/master/lecture/002/slides.html)\n\n1. Model accuracy\n1. Loss for regression and classification\n1. The variance-bias tradeoff\n1. The Bayes classifier\n1. KNN\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/master/lecture/002/slides.html) | [.pdf](https://github.com/edrubin/EC524W25/blob/master/lecture/002/slides.pdf) | [.rmd](https://github.com/edrubin/EC524W25/blob/master/lecture/002/slides.rmd)\n\n**Readings** \n\n- *ISL* Ch2–Ch3\n- *Optional:* *100ML* Preface and Ch1–Ch4\n\n[**003 - Resampling methods**](https://raw.githack.com/edrubin/EC524W25/master/lecture/003/slides.html)\n\n1. Review\n1. The validation-set approach\n1. Leave-out-out cross validation\n1. k-fold cross validation\n1. The bootstrap\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/master/lecture/003/slides.html) | [.pdf](https://github.com/edrubin/EC524W25/blob/master/lecture/003/slides.pdf) | [.rmd](https://github.com/edrubin/EC524W25/blob/master/lecture/003/slides.rmd)\n\n**Readings**\n\n- *ISL* Ch5\n- *Optional:* *100ML* Ch5\n\n[**004 - Linear regression strikes back**](https://raw.githack.com/edrubin/EC524W23/master/lecture/004/004-slides.html)\n\n1. Returning to linear regression\n1. Model performance and overfit\n1. Model selection—best subset and stepwise\n1. Selection criteria\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W23/master/lecture/004/004-slides.html) | [.pdf](https://github.com/edrubin/EC524W23/blob/master/lecture/004/004-slides.pdf) | [.Rmd](https://github.com/edrubin/EC524W23/blob/master/lecture/004/004-slides.Rmd)\n\n**Readings**\n\n- *ISL* Ch3\n- *ISL* Ch6.1\n\n**In between: `tidymodels`-ing**\n\n- [An introduction to preprocessing with `tidymodels`](https://www.kaggle.com/edwardarubin/intro-tidymodels-preprocessing). (Kaggle notebook)\n- [An introduction to modeling with `tidymodels`](https://www.kaggle.com/edwardarubin/intro-tidymodels-modeling). (Kaggle notebook)\n- [An introduction to resampling, model tuning, and workflows with `tidymodels`](https://www.kaggle.com/edwardarubin/intro-tidymodels-resampling) (Kaggle notebook)\n- [Introduction to `tidymodels`: Follow up for Kaggle](https://www.kaggle.com/edwardarubin/intro-tidymodels-split-kaggle)\n\n[**005 - Shrinkage methods**](https://raw.githack.com/edrubin/EC524W25/master/lecture/005/slides.html)\n\n(AKA: Penalized or regularized regression)\n\n1. Ridge regression\n1. Lasso\n1. Elasticnet\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/master/lecture/005/slides.html) | [.pdf](https://github.com/edrubin/EC524W25/blob/master/lecture/005/slides.pdf) | [.Rmd](https://github.com/edrubin/EC524W25/blob/master/lecture/005/slides.Rmd)\n\n**Readings**\n\n- *ISL* Ch4\n- *ISL* Ch6\n\n[**006 - Classification intro**](https://raw.githack.com/edrubin/EC524W25/master/lecture/006/slides.html)\n\n1. Introduction to classification\n1. Why not regression?\n1. But also: Logistic regression\n1. Assessment: Confusion matrix, assessment criteria, ROC, and AUC\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/master/lecture/006/slides.html) | [.pdf](https://github.com/edrubin/EC524W25/blob/master/lecture/006/slides.pdf) | [.Rmd](https://github.com/edrubin/EC524W25/blob/master/lecture/006/slides.Rmd)\n\n**Readings**\n\n- *ISL* Ch4\n\n[**007 - Decision trees**](https://raw.githack.com/edrubin/EC524W25/master/lecture/007/slides.html)\n\n1. Introduction to trees\n1. Regression trees\n1. Classification trees—including the Gini index, entropy, and error rate\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/master/lecture/007/slides.html) | [.pdf](https://github.com/edrubin/EC524W25/blob/master/lecture/007/slides.pdf) | [.rmd](https://github.com/edrubin/EC524W25/blob/master/lecture/007/slides.rmd)\n\n**Readings**\n\n- *ISL* Ch8.1–Ch8.2\n\n[**008 - Ensemble methods**](https://raw.githack.com/edrubin/EC524S24/master/lecture/008/slides.html)\n\n1. Introduction\n1. Bagging\n1. Random forests\n1. Boosting\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524S24/master/lecture/008/slides.html) | [.pdf](https://github.com/edrubin/EC524S24/blob/master/lecture/008/slides.pdf) | [.rmd](https://github.com/edrubin/EC524S24/blob/master/lecture/008/slides.rmd)\n\n**Readings**\n\n- *ISL* Ch8.2\n\n[**009 - Support vector machines**](https://raw.githack.com/edrubin/EC524S24/master/lecture/009/slides.html)\n\n1. Hyperplanes and classification\n2. The maximal margin hyperplane/classifier\n3. The support vector classifier\n4. Support vector machines\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524S24/master/lecture/009/slides.html) | [.pdf](https://github.com/edrubin/EC524S24/blob/master/lecture/009/slides.pdf) | [.rmd](https://github.com/edrubin/EC524S24/blob/master/lecture/009/slides.rmd)\n\n**Readings**\n\n- *ISL* Ch9\n\n[**010 - Dimensionality reduction and unsupervised learning**](https://raw.githack.com/edrubin/EC524W25/master/lecture/010/notebook.html)\n\n0. MNIST dataset (machines with vision)\n1. *K*-means clustering\n2. Principal component analysis (PCA)\n3. UMAP\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/master/lecture/010/notebook.html) | [.qmd](https://github.com/edrubin/EC524W22/blob/master/lecture/010/notebook.qmd)\n\n## Projects\n\nPast, present, and future projects.\n\n[**000** Predicting sales price in housing data (Kaggle)](projects/project-000)\n\n*Due:* Friday 31 January 2025 by midnight (before 11:59 PM) Pacific\n\n**Help:** \n\n- [A simple example/walkthrough](https://www.kaggle.com/edwardarubin/project-000-example)\n- [Kaggle notebooks](https://rpubs.com/Clennon/KagNotes) (from Connor Lennon)\n\n[**001** Validation and out-of-sample performance](projects/project-001)\n\n*Due:* Thursday 13 February 2025 by midnight (before 11:59 PM) Pacific\n\n[**002** Penalized regression, logistic regression, and classification](projects/project-002)\n\n*Due:* Saturday 22 February 2025 by midnight (before 11:59 PM) Pacific\n\n[**003** Trees, ensembles, and imputation](projects/project-003)\n\n*Due:* Saturday 01 March 2025 by midnight (before 11:59 PM) Pacific\n\n[Help](projects/project-003/help-003.md)\n\n[**004** Prediction finale](projects/project-004)\n\n*Due:* Wednesday 19 March 2025 by midnight (before 11:59 PM) Pacific\n\n## Class project\n\n[Outline of the project](https://github.com/edrubin/EC524W25/tree/master/projects/class-project)\n\n**Topic due by midnight on 09 February 2025**.\n\n**Final project submission due by 11:59p on 12 March 2025.**\n\n## Final exam\n\n**In-class exam**: *Monday (17 March 2025) at [8:00a–10:00a](https://registrar.uoregon.edu/dates-deadlines/exams)*\n\u003cbr\u003e\n*Note:* Previous years had a take-home portion of the final exam. This year, we will only have an in-class exam.\n\n**Prep materials**\n\u003cbr\u003e\nPrevious take-home exam: [2023](exam/past-home/home-23.md) | [2024](exam/past-home/home-24.md) \n\u003cbr\u003e\nPrevious in-class exams: [2023](exam/past-class/inclass-23.pdf) | [2024](exam/past-class/inclass-24.pdf)\n\u003cbr\u003e\n*Note:* I am not providing keys.\n\n## Lab notes\n\nApproximate/planned topics...\n\n[**000 - Workflow and cleaning**](https://raw.githack.com/edrubin/EC524W22/master/lab/000-cleaning/000-slides.html)\n\n1. General \"best practices\" for coding\n2. Working with RStudio\n3. The pipe (`%\u003e%`)\n4. Cleaning and Kaggle follow up\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W22/master/lab/000-cleaning/000-slides.html) | [.pdf](https://raw.githack.com/edrubin/EC524W22/master/lab/000-cleaning/000-slides.pdf) | [.Rmd](https://raw.githack.com/edrubin/EC524W22/master/lab/000-cleaning/000-slides.Rmd)\n\n[**001 - Workflow and cleaning: An example**](https://raw.githack.com/edrubin/EC524W25/refs/heads/master/lab/001-projects/doc001.html)\n\nFollow these steps to get started on the lab this week.\n\n1. Install Quarto. Follow this [link](https://quarto.org/docs/getting-started/installation.html), download the installer for your operating system, and follow the instructions to install Quarto\n2. Download (_and unzip_) the [Housing data](https://github.com/edrubin/EC524W22/raw/master/lab/001-cleaning/data/house-prices-advanced-regression-techniques.zip) and the [Quarto document](https://github.com/edrubin/EC524W25/blob/master/lab/001-projects/doc001.qmd) (download button top right corner of page)\n3. Create a project in RStudio in a separate folder\n4. Copy/move the data files and the Quarto document to a folder dedicated to this lab\n5. Open the Quarto document in RStudio and follow the instructions to get started on this weeks lab\n\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/refs/heads/master/lab/001-projects/doc001.html) | [.qmd](https://github.com/edrubin/EC524W25/blob/master/lab/001-projects/doc001.qmd)\n\n[**002 - Validation**](https://raw.githack.com/edrubin/EC524W25/refs/heads/master/lab/002-validation/doc002.html)\n\n1. Creating a training and validation data set from your observations dataframe in R\n2. Writing a function to iterate over multiple models to test and compare MSEs\n\n**Download**: This [zip](https://github.com/edrubin/EC524W25/raw/master/lab/002-validation/lab002.zip) file\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/refs/heads/master/lab/002-validation/doc002.html) | [.qmd](https://github.com/edrubin/EC524W25/blob/master/lab/002-validation/doc002.qmd)\n\n[**003 - Practice using `tidymodels`**](https://www.kaggle.com/edwardarubin/intro-tidymodels-preprocessing)\n\n1. Cleaning data quickly and efficiently with `tidymodels`\n\n**Formats** [.html](https://www.kaggle.com/edwardarubin/intro-tidymodels-preprocessing)\n\n[**004 - Practice using `tidymodels`**](https://www.kaggle.com/edwardarubin/intro-tidymodels-preprocessing) (continued)\n\n1. [An introduction to preprocessing with `tidymodels`](https://www.kaggle.com/edwardarubin/intro-tidymodels-preprocessing) (refresher from last week) \n2. [An introduction to modeling with `tidymodels`](https://www.kaggle.com/edwardarubin/intro-tidymodels-modeling)\n3. [An introduction to resampling, model tuning, and workflows with `tidymodels`](https://www.kaggle.com/edwardarubin/intro-tidymodels-resampling) (will finish up next week)\n\n[**005 - More practice with `tidymodels`**](https://raw.githack.com/edrubin/EC524W25/refs/heads/master/lab/003-tidymodels/doc003.html)\n\nChange an OLS workflow to a Lasso or Ridge regression workflow.\n- [Updated verion of the lab document with penalized regression](https://raw.githack.com/edrubin/EC524W25/refs/heads/master/lab/003-tidymodels/doc003-update.html)\n\n**Download**: lab project [zip file](https://github.com/edrubin/EC524W25/raw/master/lab/003-tidymodels/lab003.zip)\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/refs/heads/master/lab/003-tidymodels/doc003.html) | [.qmd](https://github.com/edrubin/EC524W25/blob/master/lab/003-tidymodels/doc003.qmd)\n\n\n**007 - Decision trees**\n\nSetting up decision trees, with and without `tidymodels`.\n\n**Download**: [Quarto document](https://github.com/edrubin/EC524W25/blob/master/lab/004-decision-trees/doc004.qmd)\n\n**Formats** [.html](https://raw.githack.com/edrubin/EC524W25/refs/heads/master/lab/004-decision-trees/doc004.html) | [.qmd](https://github.com/edrubin/EC524W25/blob/master/lab/004-decision-trees/doc004.qmd)\n\n\u003c!-- **006 - Summarizing `tidymodels`** --\u003e\n\u003c!----\u003e\n\u003c!-- 1. Summarizing `tidymodels` --\u003e\n\u003c!-- 2. [Combining pre-split data together and then defining a custom split](https://www.kaggle.com/edwardarubin/intro-tidymodels-split-kaggle) --\u003e\n\u003c!----\u003e\n\u003c!----\u003e\n\u003c!-- [**007 - Penalized regression in `tidymodels` + functions + loops**](https://raw.githack.com/edrubin/EC524W22/master/lab/006-function_loops/006_functions_loops.html) --\u003e\n\u003c!----\u003e\n\u003c!-- 1. Running a Ridge, Lasso or Elasticnet logistic regression in `tidymodels`. --\u003e\n\u003c!-- 2. [A short lesson in writing functions and loops in R)](https://raw.githack.com/edrubin/EC524W22/master/lab/006-function_loops/006_functions_loops.html) --\u003e\n\u003c!----\u003e\n\u003c!-- [**008 - Finalizing a workflow in `tidymodels`: Example using a random forest**](https://raw.githack.com/edrubin/EC524W22/master/lab/007-finalize/finalize_wf.html) --\u003e\n\u003c!----\u003e\n\u003c!-- 1. [Finalizing a workflow in `tidymodels`: Example using a random forest](https://raw.githack.com/edrubin/EC524W22/master/lab/007-finalize/finalize_wf.html) --\u003e\n\u003c!-- 2. [A short lesson in writing functions and loops in R (continued)](https://raw.githack.com/edrubin/EC524W22/master/lab/006-function_loops/006_functions_loops.html) --\u003e\n\n## Prediction in the media\n\n- NPR: [Google's new AI chatbot made a $100 billion mistake in a demo ad](https://www.npr.org/2023/02/09/1155650909/google-chatbot--error-bard-shares)\n- NYT: [Disinformation Researchers Raise Alarms About A.I. Chatbots](https://www.nytimes.com/2023/02/08/technology/ai-chatbots-disinformation.html)\n- NPR: [She was denied entry to a Rockettes show — then the facial recognition debate ignited](https://www.npr.org/2023/01/21/1150289272/facial-recognition-technology-madison-square-garden-law-new-york)\n- LA Times: [Nobody knows how widespread illegal cannabis grows are in California. So we mapped them](https://www.latimes.com/california/story/2022-09-08/how-we-mapped-illegal-cannabis-farms-in-california)\n- NYT: [Can A.I. Write Recipes Better Than Humans? We Put It to the Ultimate Test](https://www.nytimes.com/2022/11/04/dining/ai-thanksgiving-menu.html)\n- [ChatGPT](https://chat.openai.com/chat)\n  - Business Insider: [List of exams ChatGPT has passed](https://www.businessinsider.com/list-here-are-the-exams-chatgpt-has-passed-so-far-2023-1?op=1#-5)\n  - NPR: ['Everybody is cheating': Why this teacher has adopted an open ChatGPT policy](https://www.npr.org/2023/01/26/1151499213/chatgpt-ai-education-cheating-classroom-wharton-school)\n  - [How Should Schools Respond to ChatGPT?](https://www.nytimes.com/2023/01/24/learning/how-should-schools-respond-to-chatgpt.html)\n  - Energy Institute: [Can ChatGPT Save the Planet?](https://energyathaas.wordpress.com/2023/01/23/can-chatgpt-save-the-planet/)\n  - MIT Tech Review: [Here’s how Microsoft could use ChatGPT](https://www.technologyreview.com/2023/01/17/1067014/heres-how-microsoft-could-use-chatgpt/)\n  - NPR: [This 22-year-old is trying to save us from ChatGPT before it changes writing forever](https://www.npr.org/sections/money/2023/01/17/1149206188/this-22-year-old-is-trying-to-save-us-from-chatgpt-before-it-changes-writing-for)\n  - NYT: [How ChatGPT Hijacks Democracy](https://www.nytimes.com/2023/01/15/opinion/ai-chatgpt-lobbying-democracy.html)\n  - NYT: [Don’t Ban ChatGPT in Schools. Teach With It.](https://www.nytimes.com/2023/01/12/technology/chatgpt-schools-teachers.html)\n  - NYT: [How to Use ChatGPT and Still Be a Good Person](https://www.nytimes.com/2022/12/21/technology/personaltech/how-to-use-chatgpt-ethically.html)\n  - NPR: [A new AI chatbot might do your homework for you. But it's still not an A+ student](https://www.npr.org/2022/12/19/1143912956/chatgpt-ai-chatbot-homework-academia)\n  - NYT: [The Brilliance and Weirdness of ChatGPT](https://www.nytimes.com/2022/12/05/technology/chatgpt-ai-twitter.html)\n- Military applications\n  - The Drive: [M1 Abrams Tank Tested With Artificial Intelligence Targeting System](https://www.thedrive.com/the-war-zone/m1-abrams-tank-tested-with-artificial-intelligence-targeting-system)\n  - Task and Purpose: [Marines outwitted an AI security camera by hiding in a cardboard box and pretending to be trees](https://taskandpurpose.com/news/marines-ai-paul-scharre/)\n  - WP: [The next U.S. battle tank could use AI to identify targets](https://www.washingtonpost.com/technology/2022/10/12/abramsx-ai-hybrid-military-battle-tank/)\n\n## Additional resources\n\n### Jobs\n\nI wrote a very short guide to [finding a job](jobs).\n\n### R\n\n- [UO library resources/workshops](https://researchguides.uoregon.edu/library_workshops)\n- [RStudio's recommendations for learning R](https://education.rstudio.com/learn/), plus cheatsheets, books, and tutorials\n- [YaRrr! The Pirate’s Guide to R](https://bookdown.org/ndphillips/YaRrr/) (free online)\n- [Eugene R Users](https://www.meetup.com/meetup-group-cwPiAlnB/)\n\n### Data Science\n\n- [Happy Git and GitHub for the useR](https://happygitwithr.com/) by Jenny Bryan, the \"STAT 545 TAs\", and Jim Hester\n- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/) by Jake VanderPlas\n- [Elements of AI](https://course.elementsofai.com/)\n- [Caltech professor Yaser Abu-Mostafa: Lectures about machine learning on YouTube](https://www.youtube.com/user/caltech/search?query=Yaser+Abu-Mostafa)\n- From Google:\n  - [Machine-learning crash course](https://developers.google.com/machine-learning/crash-course/ml-intro)\n  - [Google Cloud training for data and machine learning](https://cloud.google.com/training/data-ml)\n  - [General Google education platform](https://ai.google/education/)\n\n### Spatial data\n\n- [Geocomputation with R](https://geocompr.robinlovelace.net) (free online)\n- [Spatial Data Science](https://keen-swartz-3146c4.netlify.com) (free online)\n- [Applied Spatial Data Analysis with R](https://asdar-book.org)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedrubin%2Fec524w25","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fedrubin%2Fec524w25","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fedrubin%2Fec524w25/lists"}