Week 1

• Monday | 2017.1.15
  • No class! MLK Day
• Wednesday | 2017.1.17
  • First day of class! Course overview and syllabus discussion.
  • Materials: [ Syllabus Slides ] [ Full Syllabus ]
  • ISL Video: [ Opening Remarks and Examples ]
  • ISL Video: [ Supervised and Unsupervised Learning ]
  • Piazza: [ STAT 432 Piazza Sign-Up ]
  • Reading: [ ISL Chapter 1 ]
  • Reading: [ ISL Chapter 2 ]
  • Additional Reading: [ Variance Explained: DS vs ML vs AI ]
• Friday | 2017.1.19
  • Re-introduction to R.
  • Materials: [ R “Intro” Slides ]
  • Lab: [ Lab 01 ] [ Lab 01 Solution ]
  • Reading: [ R4SL Chapter 3 ] [ ASR Chapters 1 - 6 ]

Week 2

• Monday | 2017.1.22
  • Introduction to rmarkdown.
  • Materials: [ rmarkdown “Intro” Slides ]
  • Lab: [ Lab 02 ] [ Lab 02 Files ] [ Lab 02 Solution ] [ Lab 02 Solution Files ]
  • Reading: [ R4SL Chapter 3 ]
  • Reading: [ ASR Chapters 1 - 6 ]
• Wednesday | 2017.1.24
  • Introduction to supervised learning, regression.
  • Reading: [ ISL Chapter 1 ]
  • Reading: [ ISL Chapter 2 ]
• Friday | 2017.1.26
  • Parametric vs non-parametric regression. Model vs tuning parameters. Model flexibility. Overfitting. Model evaluation.
  • Reading: [ ISL Chapter 2 ]
  • Deadline: [ Homework 00 Due ]

Week 3

• Monday | 2017.1.29
  • Regression lab.
  • Lab: [ Lab 03 ] [ Lab 03 Files ] [ Lab 03 Solution ] [ Lab 03 Solution Files ]
• Wednesday | 2017.1.31
  • Begin recap of linear models, focusing on making predictions.
  • Reading: ISL 3.1 - 3.4
  • Reading: [ R4SL Chapter 4 ]
  • Reading: [ R4SL Chapter 6 ]
  • Reading: [ ASR Chapters 7 - 15 ]
  • ISL Slides: [ Linear Regression ]
  • ISL Video: [ Simple Linear Regression ]
  • ISL Video: [ Hypothesis Testing ]
  • ISL Video: [ Interpreting Regression Coefficients ]
  • ISL Video: [ Model Selection and Qualitative Predictors ]
  • ISL Video: [ Interactions and Nonlinearity ]
• Friday | 2017.2.2
  • lm() recap.
  • Deadline: [ Homework 01 Due ]

Week 4

• Monday | 2017.2.5
  • lm() lab.
  • Lab: [ Lab 04 ] [ Lab 04 Files ] [ Lab 04 Solution ] [ Lab 04 Solution Files ]
• Wednesday | 2017.2.7
  • Bias-Variance Tradeoff
  • Reading: [ R4SL Chapter 8 ]
  • Slides: [ Bias-Variance Tradeoff ]
• Friday | 2017.2.9
  • Some knnreg() details.
  • Deadline: [ Homework 02 Due ]

Week 5

• Monday | 2017.2.12
  • knnreg() lab.
  • Lab: [ Lab 05 ] [ Lab 05 Files ] [ Lab 05 Solution ] [ Lab 05 Solution Files ]
• Wednesday | 2017.2.14
  • Start classification.
  • Reading: ISL 4.1
  • Reading: [ R4SL Chapter 9 ]
• Friday | 2017.2.16
  • Introduction to logistic regression.
  • Reading: ISL 4.2 - 4.3
  • Reading: [ R4SL Chapter 10 ]
  • Reading: [ ASR Chapter 17 ]
  • ISL Slides: [ Classification ]
  • ISL Video: [ Introduction to Classification ]
  • ISL Video: [ Logistic Regression ]
  • ISL Video: [ Multiple Logistic Regression ]
  • Deadline: [ Homework 03 Due ]

Week 6

• Monday | 2017.2.19
  • More logistic regression.
  • Supplement: [ Logistic Regression Exercises ]
• Wednesday | 2017.2.21
  • Classification with KNN. Categorical response.
  • Reading: R4SL Chapter 10
  • Reading: R4SL Chapter 12
  • Supplement: [ Classification with KNN in R ] [ .Rmd File ]
• Friday | 2017.2.23
  • Generative models in R.
  • Reading: ISL 4.4
  • Reading: [ R4SL Chapter 11 ]
  • Deadline: [ Homework 04 Due ]

Week 7

• Monday | 2017.2.26
  • Mathematical details of LDA with one predictor.
  • Location: 119 MSEB
  • Reading: ISL 4.4
  • Reading: [ R4SL Chapter 11 ]
  • ISL Video: [ Linear Discriminant Analysis and Bayes Theorem ]
  • ISL Video: [ Univariate Linear Discriminant Analysis ]
• Wednesday | 2017.2.28
  • Mathematical details of LDA and QDA with multiple predictors. Review for quiz.
  • Reading: ISL 4.4
  • Reading: [ R4SL Chapter 11 ]
  • Supplement: [ LDA Exercises ]
  • ISL Video: [ Multivariate Linear Discriminant Analysis ]
  • ISL Video: [ Quadratic Discriminant Analysis and Naive Bayes ]
• Friday | 2017.3.2
  • Finish generative methods, including Naive Bayes. Review for quiz.
  • Reading: ISL 4.4
  • Reading: [ R4SL Chapter 11 ]
  • Deadline: [ Homework 05 Due ]

Week 8

• Monday | 2017.3.5
  • Review.
• Wednesday | 2017.3.7
  • QUIZ ONE
• Friday | 2017.3.9
  • Cross-validation.
  • Reading: ISL 5.1
  • Reading: [ R4SL Chapter 20 ]
  • ISL Slides: [ Resampling ]
  • ISL Video: [ Validation Set Approach ]
  • ISL Video: [ k-fold Cross-Validation ]
  • ISL Video: [ Cross-Validation: The Right and Wrong Ways ]

Week 9

• Monday | 2017.3.12
  • The caret package. Machine learning pipelines.
  • Individual project details released.
  • Reading: [ R4SL Chapter 21 ]
• Wednesday | 2017.3.14
  • Some comments on variable selection. Entering modernity.
  • Reading: ISL 6.1
  • Reading: [ R4SL Chapter 22 ]
  • ISL Slides: [ Model Selection ]
  • ISL Video: [ Best Subset Selection ]
  • ISL Video: [ Forward Stepwise Selection ]
  • ISL Video: [ Backward Stepwise Selection ]
  • ISL Video: [ Estimating Test Error I ]
  • ISL Video: [ Estimating Test Error II ]
• Friday | 2017.3.16
  • More modernity. Introducing regularization.
  • Reading: ISL 6.2
  • Reading: [ R4SL Chapter 24 - Regularization ]
  • ISL Slides: [ Model Selection ]
  • ISL Video: [ Shrinkage Methods and Ridge Regression ]
  • ISL Video: [ The Lasso ]
  • ISL Video: [ Tuning Parameter Selection ]
  • Deadline: [ Homework 06 Due ]

Week 10

• Monday | 2018.3.19
  • Spring Break
• Wednesday | 2018.3.21
  • Spring Break
• Friday | 2018.3.23
  • Spring Break

Week 11

• Monday | 2018.3.26
  • More on ridge and lasso. Using ridge and lasso in R.
  • Reading: ISL 6.2
  • Reading: [ R4SL Chapter 24 - Regularization ]
• Wednesday | 2018.3.28
  • Elastic net.
  • Reading: [ R4SL Chapter 25 - Elastic Net ]
• Friday | 2018.3.30
  • Introduction to trees.
  • Reading: ISL 8.1
  • Reading: [ R4SL Chapter 26 - Trees ]
  • ISL Slides: [ Trees ]
  • Additonal Slides: [ Tree-based Methods ]
  • ISL Video: [ Decision Trees ]
  • ISL Video: [ Pruning a Decision Tree ]
  • ISL Video: [ Classification Trees and Comparison with Linear Models ]
  • Deadline: Individual Project

Week 12

• Monday | 2018.4.2
  • Reading: ISL 8.2
  • R4SL: [ R4SL Chapter 27 - Ensemble Methods ]
  • ISL Slides: [ Trees ]
  • Additonal Slides: [ Ensemble Methods: Bagging, Boosting ]
  • ISL Video: [ Bootstrap Aggregation (Bagging) and Random Forests ]
  • ISL Video: [ Boosting and Variable Importance ]
• Wednesday | 2018.4.4
  • Some tree and ensemble details.
• Friday | 2018.4.6
  • Extensions of random forests and boosting, in R. Some summary of supervised learning.
  • Additional Slides: [ Supervised Learning Review ]
  • Reading: [ Extremely Randomized Trees, Ranger, XGBoost ] [ rmarkdown ]
  • Reading: [ Statistical Modeling: The Two Cultures ]
  • Reading: [ Do we Need Hundreds of Classifiers to Solve Real World Classification Problems? ]
  • Deadline: [ Homework 07 Due ]

Week 13

• Monday | 2018.4.9
  • Review of individual project analyses.
• Wednesday | 2018.4.11
  • Class “cancelled.” Use this time for a group meeting.
• Friday | 2018.4.13
  • Review of individual project analyses.
  • Deadline: Group Project Proposals
  • Deadline: Graduate Project

Week 14

• Monday | 2018.4.16
  • Unsupervised learning. Clustering.
  • Reading: ISL 10.1 - 10.3
  • R4SL: [ Unsupervised Learning ] ]
  • ISL Slides: [ Unsupervised Learning ]
  • Additional Slides: [ Unsupervised Learning, Clustering ]
  • ISL Video: [ Unsupervised Learning and Principal Components Analysis ]
  • ISL Video: [ Exploring Principal Components Analysis and Proportion of Variance Explained ]
  • ISL Video: [ K-means Clustering ]
  • ISL Video: [ Hierarchical Clustering ]
• Wednesday | 2018.4.18
  • Brief thoughts on PCA. Some fun with K-means.
  • Additional Slides: [ Unsupervised Learning, Dimension Reduction ]
• Friday | 2018.4.20
  • Class canceled. (Project work time.)
  • Office hours canceled. (Sorry.)
  • Deadline: [ Homework 08 Due ]

Week 15 - Current Week

• Monday | 2018.4.23
  • Review for quiz. Discussion competition results.
• Wednesday | 2018.4.25
  • QUIZ TWO
• Friday | 2018.4.27
  • Class canceled. (Project work time.)
  • Deadline: None

Week 16

• Monday | 2018.4.30
  • Course wrap-up. Return quizzes. Discuss the “Big Picture” and how to continue learning ML.
  • Reading: [ Machine Learning and Data Science Resources ]
• Wednesday | 2018.5.2
  • No class. Work on projects!
• Friday | 2018.5.4
  • No class. Finals times!
  • Final office hours.

Week 17

• Wednesday | 2018.5.9
  • No office hours during finals week.
  • Deadline: Group Project

Homework

Quizzes

Projects