Course Materials

Unit 1: Overview

Reading

• Yarkoni and Westfall (2017) paper
• James et al. (2023) Chapter 2, pp 15 - 42

Slide decks

• Lecture
• Discussion

Videos

• Lecture 1: An Introductory Framework ~ 9 mins

• Lecture 2: More Details on Supervised Techniques ~ 23 mins

• Lecture 3: Key Terminology in Context ~ 11 mins

• Lecture 4: An Example of Bias-Variance Tradeoff ~ 27 mins

• Discussion - Tuesday

• Discussion - Thursday

Lab Materials

• None this week

Application Assignment

• No assignment this week

Quiz

• Submit the unit quiz by 8 pm on Wednesday, January 22nd

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Unit 2: Exploratory Data Analysis

Reading

[NOTE: These are short chapters. You are reading to understand the framework of visualizing data in R. Don’t feel like you have to memorize the details. These are reference materials that you can turn back to when you need to write code!]

• Wickham, Çetinkaya-Rundel, and Grolemund (2023) Chapter 1, Data Visualization
• Wickham, Çetinkaya-Rundel, and Grolemund (2023) Chapter 9, Layers
• Wickham, Çetinkaya-Rundel, and Grolemund (2023) Chapter 10, Exploratory Data Analysis

Slide decks

• Lecture
• Discussion

Videos

• Lecture 1: Stages of Data Analysis and Model Development ~ 10 mins

• Lecture 2: Best Practices and Other Recommendations ~ 27 mins

• Lecture 3: EDA for Data Cleaning ~ 41 mins

• Lecture 4: EDA for Modeling - Univariate ~ 24 mins

• Lecture 5: EDA for Modeling - Bivariate ~ 20 mins

• Lecture 6: Working with Recipes ~ 13 mins

• Discussion

• Lab w/Zihan

Lab Materials (Zihan - Jan 28th)

• Lab 1 w/Zihan

Application Assignment

• data

• data dictionary

• cleaning EDA: qmd

• modeling EDA: qmd

• solutions: cleaning EDA; modeling EDA

• Submit the application assignment by 8 pm on Wednesday, January 29th.

Quiz

• Submit the unit quiz by 8 pm on Wednesday, January 29th.

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Unit 3: Introduction to Regression Models

Reading

• James et al. (2023) Chapter 3, pp 59 - 109

Slide decks

• Lecture
• Discussion

Videos

• Lecture 1: Overview ~ 13 mins

• Lecture 2: The Simple Linear Model, Part 1 ~ 16 mins

• Lecture 3: The Simple Linear Model, Part 2 ~ 12 mins

• Lecture 4: Extension to Multiple Predictors ~ 15 mins

• Lecture 5: Extension to Categorical Predictors ~ 30 mins

• Lecture 6: Extension to Interactions and Non-Linear Effects ~ 11 mins

• Lecture 7: Introduction to KNN ~ 9 mins

• Lecture 8: The hyperparameter k ~ 13 mins

• Lecture 9: Distance and Scaling in KNN ~ 9 mins

• Lecture 10: KNN with Ames ~ 12 mins

• Discussion

• Lab w/Coco - Feb 4th

Lab Materials

• Lab 2 w/Coco - Feb 4th

Application Assignment

• clean data: train; validate; test

• data dictionary

• lm qmd

• knn qmd

• solution: lm; knn

• Submit the application assignment by 8 pm on Wednesday, February 5th.

Quiz

• Submit the unit quiz by 8 pm on Wednesday, February 5th.

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Unit 4: Introduction to Classification Models

Reading

• James et al. (2023) Chapter 4, pp 129 - 164

Slide decks

• Lecture
• Discussion

Videos

• Lecture 1: The Bayes Classifier ~ 9 mins

• Lecture 2: Conceptual Overview of Logistic Regression ~ 19 mins

• Lecture 3: EDA with the Cars Dataset ~ 12 mins

• Lecture 4: Logistic Regression with Cars Dataset ~ 32 mins

• Lecture 5: KNN with Cars Dataset ~ 19 mins

• Lecture 6: LDA, DQA, RDA with Cars Dataset ~ 16 mins

• Lecture 7: Comparisons among Classifiers ~ 11 mins

• Discussion

• Lab w/Zihan - Feb 11

Lab Materials

• Lab 3 w/Zihan - Feb 11th

Application Assignment

• data: raw; test

• data dictionary

• shells: cleaning EDA qmd; rda qmd; knn qmd

• solution: modeling EDA; rda; knn

• Submit the application assignment by 8 pm on Wednesday, February 12th.

Quiz

• Submit the unit quiz by 8 pm on Wednesday, February 12th.

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Unit 5: Resampling Methods for Model Selection and Evaluation

Reading

• Kuhn and Johnson (2018) Chapter 4, pp 61 - 80

• Supplemental: James et al. (2023) Chapter 5, pp 197 - 208 186

Slide decks

• Lecture
• Discussion

Videos

• Lecture 1: Overview & Parallel Processing ~ 16 mins

• Lecture 2: Introduction to Resampling ~ 11 mins

• Lecture 3: Single Validation/Test Set Approach ~ 26 mins

• Lecture 4: Leave One Out Cross Validation ~ 9 mins

• Lecture 5: K-fold Cross Validation Approaches ~ 21 mins

• Lecture 6: Repeated and Grouped K-fold Approaches ~ 11 mins

• Lecture 7: Bootstrap Resampling ~ 11 mins

• Lecture 8: Using Resampling to Select Best Model Configurations ~ 17 mins

• Lecture 9: Resampling for Both Model Selection and Evaluation ~ 11 mins

• Lecture 10: Nested Resampling ~ 14 mins

• Discussion

• Lab

Lab Materials

• Lab - Feb 18 w/Coco

Application Assignment

• data

• data dictionary

• qmd shell

• solution

• Submit the application assignment by 8 pm on Wednesday, February 19th.

Quiz

• Submit the unit quiz by 8 pm on Wednesday, February 19th.

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Unit 6: Regularization and Penalized Models

Reading

• James et al. (2023) Chapter 6, pp 225 - 267

Slide decks

• Lecture
• Discussion

Videos

• Lecture 1: An Introduction to Penalized/Regularized Algorithms ~ 15 mins

• [Lecture 2: Intuitions about Penalized Cost Functions and Regularization ~ 11 mins

• Lecture 3: Ridge Regression ~ 9 mins

• Lecture 4: LASSO ~ 8 mins

• Lecture 5: The Elastic net ~ 4 mins

• Lecture 6: Emprical Example - Many good predictors ~ 23 mins

• Lecture 7: Emprical Example - Good and zero predictors ~ 9 mins

• Lecture 8: Emprical Example - LASSO for covariate selection ~ 8 mins

• Discussion

• Lab w/Zihan - Feb 25th

Lab Materials

• Lab - Feb 25
• Lab - Feb 25 (HTML)
• Nested Cross-validation Example

Application Assignment

• data

• data dictionary

• qmd shell

• solution

• Submit the application assignment by 8 pm on Wednesday, February 26th.

Quiz

• Submit the unit quiz by 8 pm on Wednesday, February 26th.

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Unit 8: Advanced Performance Metrics

Reading

• Kuhn and Johnson (2018) Chapter 11, pp 247-266
• Kuhn and Johnson (2018) Chapter 16, pp 419-435
• Wyant et al, in press

Slide decks

• Lecture
• Discussion

Videos

• Lecture 1: Unit Introduction ~ 15 mins

• Lecture 2: The Confusion Matrix, Part 1~ 33 mins

• Lecture 3: The Confusion Matrix, Part 2~ 11 mins

• Lecture 4: The Receiver Operating Characteristic (ROC) Curve ~ 25 mins

• Lecture 5: Selecting Model Configurations with Other Metrics ~ 10 mins

• Lecture 6: Addressing Class Imbalance ~ 24 mins

• Discussion

• Lab - March 11th

Lab Materials

• Lab
• Lab (HTML)

Application Assignment

• data

• qmd shell

• solution

• Submit the application assignment by noon on Friday, March 14th.

Quiz

• Submit the unit quiz by 8 pm on Wednesday, March 12th.

References

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2023. An Introduction to Statistical Learning: With Applications in R. 2nd ed. Springer Texts in Statistics. New York: Springer-Verlag.

Kuhn, Max, and Kjell Johnson. 2018. Applied Predictive Modeling. 1st ed. 2013, Corr. 2nd printing 2018 edition. New York: Springer.

Wickham, Hadley, Çetinkaya-Rundel Mine, and Garrett Grolemund. 2023. R for Data Science: Visualize, Model, Transform, and Import Data. 2nd ed. https://r4ds.hadley.nz/.

Yarkoni, Tal, and Jacob Westfall. 2017. “Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.” Perspectives on Psychological Science 12 (6): 1100–1122.