**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**]

- Re-introduction to

**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**]

- Introduction to
**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 ]

**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 ]

**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 ]

- Some

**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 ]

**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 ]

- Generative models in

**Monday**| 2017.2.26- Mathematical details of LDA with one predictor.
: 119 MSEB*Location**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 ]

**Monday**| 2017.3.5- Review.

**Wednesday**| 2017.3.7**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 ]

**Monday**| 2017.3.12- The
`caret`

package. Machine learning pipelines. - Individual project details released.
*Reading:*[ R4SL Chapter 21 ]

- The
**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 ]

**Monday**| 2018.3.19- Spring Break

**Wednesday**| 2018.3.21- Spring Break

**Friday**| 2018.3.23- Spring Break

**Monday**| 2018.3.26- More on ridge and lasso. Using ridge and lasso in
`R`

. *Reading:*ISL 6.2*Reading:*[ R4SL Chapter 24 - Regularization ]

- More on ridge and lasso. Using ridge and lasso in
**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

**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 ]

- Extensions of random forests and boosting, in

**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

**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 ]

**Monday**| 2018.4.23- Review for quiz. Discussion competition results.

**Wednesday**| 2018.4.25**Friday**| 2018.4.27- Class canceled. (Project work time.)
- Deadline: None

**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.

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

- Date: Wednesday, March 7
- Practice: [ Practice Problems ]
- Practice: [ Extra Practice Problems ]

- Due: Sunday, April 1
- [ Project Information ]
- [ Project Data ]

- Due: Friday, April 13
- [ Project Information ]