Last updated: May 13. 12:00PM (central)
Zoom link is on Canvas.
- Lecture 5 and Rmd
- For those of you struggling with homeworks, there is a lot of valuable code in the old Lecture Rmd files
- Semiconductors: Data, code in lecture Rmd
- Comscore: Code in lecture Rmd, Data: domains, sites, total spend
- Lecture 6 and Rmd
- The last few slides include a lot of useful code. If you can interpret and run the cross-validation code there, you should gain a solid grasp on cross-validation generally.
- Homework 3 and Rmd
- Due Next wednesday at midnight.
- Solutions and Rmd
- Some of the ratios etc may be inverted. This shouldn’t change any interpretations.
- Textbook References
- AIC: ESL Ch 7.5
- BIC: ESL Ch 7.7
- Stepwise: ESL Ch. 3.3
- LASSO/Ridge/Shrinkage: ESL Ch. 3.4, 3.6, 3.8
- Cross-Validation: ESL Ch 7.10
- Bias-Variance Decomp: ESL Ch 7.2, 7.3
- Lecture 3: Regression. Rmd
- Homework 2 – Due Wednesday April 14
- Lecture 4 and Rmd
- Textbook References:
- Linear Regression: ESL Ch. 3.2
- Logistic Regression: ESL Ch. 4.4
- Deviance: ESL p. 124
- Out-of-Sample: ESL Ch. 7.1 (uses the term ‘generalization error’ or ‘validation error’)
- Bootstrap: ESL Ch 7.49
- Lecture 1: Admin, Syllabus, Stats 101, FDR Control
- HW1a – Prediction Competition.
- Lecture 2: Predictions, FDR Control, and Loss.
- Lipids example:
- Data is on canvas under lecture 2. “jointGwasMc_LDL.txt”
- Diabetes Example:
- Simulations in lecture: Code
- Homework 1: FDR control
- Textbook references:
- False discovery rate: Ch. 18.7 of ESL
- Loss: Ch. 2.4, Ch. 7.2, and Ch. 10.6 of ESL
- “Elements of Statistical Learning” (ESL) by Hastie, Tibshirani, and Friedman – online and here
- “Statistical Consequences of Fat Tails” by NN Taleb – online and here
- “Causal Inference: The Mixtape” by Scott Cunningham – online
- HW0: An ungraded assignment for making sure everyone is up to speed. Entirely optional.
Questions? Email me (firstname.lastname@example.org) and I’ll help you out.