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Statistical Learning

Week 1

Monday

No class

Wednesday

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

Week 2

Monday

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

Wednesday

  • 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

Week 3

Monday

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

Wednesday

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

Week 4

Monday

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

Wednesday

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

Week 5

Monday

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

Wednesday

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

Week 6

Monday

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

Wednesday

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

Week 7

Monday

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

Wednesday

  • Midterm I

Week 8

Monday

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

Wednesday

  • Bootstrap

Week 9

Monday

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

Wednesday

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

Week 10

Monday

  • 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)

Wednesday

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