Click on the text like “Week 1: Sep 02 – Sep 06” to expand or collapse the items we covered in that week.

I will fill in more detail and provide links to lecture notes and labs as we go along. Items for future dates are tentative and subject to change.

#### Fri, Sep 06

• In class, we will work on:
• Polynomial Regression (Section 3.3 of ISLR): pdf
• Motivate model selection by minimizing Residual Sum of Squares (Section 3.1 of ISLR): pdf
• Continue reading Chapter 1, and Sections 2.1, 2.2.1, 2.2.2, 3.1, 3.2, and 3.3 of ISLR.
• Start on Homework 1, due Friday Sep 13; assigned on GitHub.

#### Mon, Sep 09

• In class, we will work on:
• Multiple regression in matrix notation: pdf
• Model estimation by minimizing Residual Sum of Squares (Sections 3.1.1 and 3.2.1 of ISLR)
• Lecture notes: pdf. Note that our derivation in terms of matrices is not discussed in our text book. :(
• Continue reading Chapter 1, and Sections 2.1, 2.2.1, 2.2.2, 3.1, 3.2, and 3.3 of ISLR.

#### Wed, Sep 11

• In class, we will work on:
• Finish second example from last class.
• Review of orthogonal projections
• Lecture notes: pdf
• Matrix/linear algebra view of fitted values
• $$H = X (X'X)^{-1} X'$$ is the hat matrix.
• $$\hat{y} = X \hat{\beta} = Hy$$ is the orthogonal projection of y into the column space of $$X$$.
• ANOVA Example: html
• Work on HW1, due Friday Sep 13

#### Fri, Sep 13

• In class, we will work on:
• R commands for doing matrix operations .R
• What it means for X to not have full column rank
• Handout: pdf
• Interactive plots for first example: html
• If time, start a worksheet that will be part of your next homework assignment: pdf
• Do HW2, due Friday Sep 20, to be assigned on GitHub some time Saturday

#### Mon, Sep 16

• In class, we will work on:
• Train/test splits:
• Motivating example: pdf
• Example wrap up: pdf
• Lab 01, about train/test splits
• Read Sections 5.1.1 through 5.1.4 of ISLR
• Work on HW2, due Friday Sep 20

#### Fri, Sep 20

• In class, we will work on:
• Pairs Plots: pdf
• Work on labs

#### Mon, Sep 23

• In class, we will work on:
• Expected test set MSE and the Bias/Variance trade-off.
• Slides: pdf
• Source for Slides: Rmd
• Finish Lab 2
• Study for quiz on Wed

#### Fri, Sep 27

• In class, we will work on:
• Transformations: pdf

#### Mon, Sep 30

• In class, we will work on:

#### Fri, Oct 04

• In class, we will work on:
• Measuring error rates and cross-validation for classification (continuing lecture notes from last class)
• Start handout from last class

#### Wed, Oct 09

• In class, we will work on:
• Midterm

#### Fri, Oct 11

• In class, we will work on:
• Answer to problem 2 (a) and (b) on HW

#### Mon, Oct 14

• No Class: Midsemester Break. Safe travels!

#### Wed, Oct 16

• In class, we will work on:
• Logistic Regression
• Lecture notes: pdf
• Handout: pdf

#### Fri, Oct 18

• In class, we will work on:
• Logistic Regression with multiple explanatory variables pdf

#### Mon, Oct 21

• In class, we will work on:
• Finish handout on logistic regression with multiple explanatory variables from last class.
• Estimation for logistic regression: pdf

#### Wed, Oct 23

• In class, we will work on:
• Measures of classification skill: pdf
• Work on Lab 5
• Lab 5 due Fri Oct 25
• HW 5 due Wed Oct 30

#### Fri, Oct 25

• In class, we will work on:
• Multinomial Logistic Regression and AIC
• Lecture notes pdf
• Handout about multnomial logistic regression pdf

#### Mon, Oct 28

• In class, we will work on:
• Penalized estimation
• hand out: pdf
• we also defined the LASSO regression estimator as minimizing RSS + $$\lambda \sum_{j = 1}^p \vert\beta_j\vert$$, and ridge regression as minimizing RSS + $$\lambda \sum_{j=1}^p \beta_j^2$$
• Work on HW due Wed Oct 30

#### Wed, Oct 30

• In class, we will work on:
• Classification and regression trees
• lecture notes: pdf
• hand out: pdf

#### Fri, Nov 01

• In class, we will work on:
• Quiz
• Overview of where we’ve been and where we’re going: pdf

#### Mon, Nov 04

• In class, we will work on:
• Start on ensembles and stacking:
• Lecture notes: pdf
• Stacking schematic: pdf

#### Wed, Nov 06

• In class, we will work on:
• Code for stacking:
• Classification example: pdf
• Regression example: pdf
• Prepare for quiz on Friday

#### Fri, Nov 08

• In class, we will work on:
• Quiz on logistic regression and classification error
• Start on bagging, feature subsets, and random forests
• lecture notes: pdf
• hand out: pdf

#### Mon, Nov 11

• In class, we will work on:
• Finish bagging, feature subsets, and random forests

#### Wed, Nov 13

• In class, we will work on:
• quiz on trees and penalized estimation
• Start on gradient tree boosting
• hand out: pdf
• lecture notes: pdf

#### Fri, Nov 15

• In class, we will work on:

#### Mon, Nov 18

• In class, we will work on:

#### Wed, Nov 20

• In class, we will work on:

#### Fri, Nov 22

• In class, we will work on:

#### Mon, Nov 25

• In class, we will work on:

#### Wed, Nov 27

• No Class: Thanksgiving Break. Safe travels!

#### Fri, Nov 29

• No Class: Thanksgiving Break. Safe travels!

#### Mon, Dec 02

• In class, we will work on:

#### Wed, Dec 04

• In class, we will work on:
• Evan is away at a conference.
• Time to work on your projects in groups.

#### Fri, Dec 06

• In class, we will work on:
• Evan is away at a conference.
• Time to work on your projects in groups.