No class

• Course Logistics
• Guess My Age
• For next time:
  • Sign up for slack group
  • Set up github account
  • Read p. 1 - 14

• Github portfolios
• Estimating f
• For next time:
  • Read p 15 - 41
  • Problem Set 1 due beginning of next class

• Decomposing MSE
• Bias - Variance Tradeoff
• If time: start Lab 1
• For next time:
  • Read p 59 - 81
  • Lab 1 due by start of next class

• k-nearest neighbors (KNN)
• Linear Regression
• For next time:
  • Read p 82 - 92
  • Lab 2 due at beginning of class next Monday

• Extending the linear model
• For next time:
  • Read p 92 - 109
  • Finish Lab 2

• Review MSE for KNN (from Lab 2)
• Geometry of MLR (housing prices activity)
• Assesssing Model Fit
• For next time:
  • Work on Lab 3

• Diagnostics
  • Model Validity
  • Outliers
  • Transformations
  • Multicollinearity
• For next time:
  • Lab 3 due Friday at noon

• Regression Competition Results
• Automated Model Selection
• For next time:
  • Revise lab-03.Rmd according to Activity at end of slides
  • Read p. 203 - 227

• Penalized Regression
  • Ridge
  • Lasso
• For next time:
  • Lab 4

• Classification
  • KNN
  • Logistic Regression
• For next time:
  • Read p. 127 - 149

• Discriminant Analysis
• For next time:
  • Lab 5 due Wednesday
  • Read p. 149 - 154

• Classification Errors
• Extending Discriminant Analysis
• For next time:
  • Lab 5 due
  • Study!

• Midterm I

• Resampling
  • Validation sets
  • Leave-one-out CV
  • k-fold CV
• For next time:
  • Read p. 175-197

• Bootstrap

• Regression Trees
• For next time:
  • Read p. 303-321

• Classification Trees
• For next time:
  • Read p. 321-324
  • Group Proposals due 11:59 pm Thursday

• Collaborating with GitHub
• Empowering the Tree
  • Bagging
  • Variable Importance
  • Random Forests
• For next time:
  • Read p. 321-324
  • Write out answers to reading questions for Breiman (see slides)

• Empowering the Tree
  • Boosting (by hand)
• Discussion of "Two Cultures"