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

Application Assignment

• No assignment this week

Quiz

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

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

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.

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

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 5thj.

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.

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.