Last updated: 2021-05-29 10:31:47

### Week 9

• Lecture 17 – Neural Nets, SGD, Optimization – Rmd
• Lecture 18 – Review of Course – Rmd
• Textbook refs: Neural Nets – ESL Ch. 11

### Week 5

• Lecture 9 – ROC and Trees – Rmd
• Lecture 10 – Trees and Forests – and Rmd
• Textbook References:
• Trees: ESL Ch. 9.2
• Bagging: ESL Ch 8.7
• Forests: ESL Ch 15

### Week 3

• Lecture 5 – Variable Selection – Rmd and PDF
• 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 – Cross Validation – Rmd and PDF
• 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

### Week 2

• Lecture 3 – Regression: Linear and Logit – Rmd and PDF
• Homework 2 – Due Wednesday April 14
• Lecture 4 – Deviance, OOS, Bootstrap – Rmd and PDF
• 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

### Before Class

• Syllabus.
• Textbooks:
• “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.