**Monday**| 2017.8.28- First day of class! Course overview and syllabus discussion.
*Materials*: Syllabus Slides, Full Syllabus*ISL Videos*: Opening Remarks and Examples, Supervised and Unsupervised Learning

**Wednesday**| 2017.8.30- Quick probability review. Recapping some
`R`

basics. *Reading*: R4SL Chapter 2, R4SL Chapter 3*Slides*: Probability Recap,`R`

Introduction*Lab*:`R`

Basics,`R`

Basics Solutions

- Quick probability review. Recapping some
**Friday**| 2017.9.1- Introduction to
`rmarkdown`

. *Slides*:`rmarkdown`

Introduction

- Introduction to

**Monday**| 2017.9.4**No class!**Labor Day

**Wednesday**| 2017.9.6- More
`rmarkdown`

details and practice. What is a model? *Reading*: R4SL Chapter 3*Lab*:`rmarkdown`

,`rmarkdown`

Solutions

- More
**Friday**| 2017.9.8- Begin recap of regression basics.
*Reading*: ISL 3.1 - 3.4, R4SL Chapter 4*ISL Slides*: Linear Regression*ISL Videos*: Simple Linear Regression, Hypothesis Testing, Interpreting Regression Coefficients, Model Selection and Qualitative Predictors, Interactions and Nonlinearity*Deadline*: Homework 00 Due

**Monday**| 2017.9.11- Review using
`lm()`

for regression models in`R`

. *Reading*: R4SL Chapter 4

- Review using
**Wednesday**| 2017.9.13- Introduce the supervised learning, regression, task. Discuss the test-train split and models that generalize to unseen data.
*Reading*: ISL 2.1 - 2.2*ISL Slides*: Statistical Learning*ISL Videos*: Statistical Learning and Regression, Assessing Model Accuracy and Bias-Variance Trade-off*Lab*: Test-Train Split, Test-Train Split Solutions

**Friday**| 2017.9.15- Continue discussion of regression in the context of statistical learning.
*Slides*: Linear Models for Statistical Learning, Regression*Deadline*: Homework 01 Due

**Monday**| 2017.9.18- Introduce KNN. Compare non-parametric methods to parametric methods. Discuss tuning parameters versus model parameters.
*Reading*: R4SL Chapter 7 (Currently very sparse notes.)

**Wednesday**| 2017.9.20- Continue discussion of KNN. Compare KNN to linear models. Some live coding examples.

**Friday**| 2017.9.22- Finish discussion of KNN
*Deadline*: Homework 02 Due

**Monday**| 2017.9.25- Bias-Variance Tradeoff
*Reading*: R4SL Chapter 8*Slides*: Bias-Variance Tradeoff

**Wednesday**| 2017.9.27- Begin classification.
*Reading*: ISL 4.1, R4SL Chapter 9*Slides*: Classification Introduction

**Friday**| 2017.9.29- More classification. Introduction to logistic regression
*Reading*: ISL 4.2 - 4.3, R4SL Chapter 10*ISL Slides*: Classification*ISL Videos*: Introduction to Classification*Deadline*: Homework 03 Due

**Monday**| 2017.10.2- Continued discussion of logistic regression.
*Reading*: ISL 4.3, R4SL Chapter 10*ISL Slides*: Classification*ISL Videos*: Logistic Regression, Multiple Logistic Regression

**Wednesday**| 2017.10.4- KNN for classification.
*Reading*: R4SL Chapter 12

**Friday**| 2017.10.6- Generative methods in
`R`

. *Reading*: ISL 4.4, R4SL Chapter 11*Deadline*: Homework 04 Due

- Generative methods in

**Monday**| 2017.10.9- Continued discussion of generative methods. Details of univariate LDA.
*Reading*: ISL 4.4, R4SL Chapter 11*ISL Slides*: Classification*ISL Videos*: Linear Discriminant Analysis and Bayes Theorem, Univariate Linear Discriminant Analysis

**Wednesday**| 2017.10.11- Continued discussion of generative methods. Multivariate LDA, QDA, Naive Bayes.
*Reading*: ISL 4.4, R4SL Chapter 11*ISL Videos:*Multivariate Linear Discriminant Analysis, Quadratic Discriminant Analysis and Naive Bayes

**Friday**| 2017.10.13- Some final thoughts on generative methods. Some recap of classification methods. Some
`R`

details. *Deadline*: Homework 05 Due

- Some final thoughts on generative methods. Some recap of classification methods. Some

**Monday**| 2017.10.16- Review.

**Wednesday**| 2017.10.18**Friday**| 2017.10.20- Begin discussing Statistical Learning in practice.
*Deadline*: None. No homework during quiz week.

**Monday**| 2017.10.23- Cross-validation and
`caret`

. *Reading:*ISL 5.1, R4SL Chapter 20, R4SL Chapter 21*ISL Slides:*Resampling*ISL Videos:*Validation Set Approach, k-fold Cross-Validation, Cross-Validation: The Right and Wrong Ways

- Cross-validation and
**Wednesday**| 2017.10.25- More cross-validation and
`caret`

.

- More cross-validation and
**Friday**| 2017.10.27- More cross-validation and
`caret`

. *Deadline*: Homework 06 Due

- More cross-validation and

**Monday**| 2017.10.30- Some comments on variable selection.
*Reading:*ISL 6.1, R4SL Chapter 22*ISL Slides:*Model Selection*ISL Videos:*Best Subset Selection, Forward Stepwise Selection, Backward Stepwise Selection, Estimating Test Error I, Estimating Test Error II

**Wednesday**| 2017.11.1- Entering the modern age. Introducing regularization.
*Reading:*ISL 6.2, R4SL Chapter 24 - Regularization*ISL Slides:*Model Selection*ISL Videos:*Shrinkage Methods and Ridge Regression, The Lasso, Tuning Parameter Selection

**Friday**| 2017.11.3- More on ridge and lasso. Using ridge and lasso in
`R`

. *Reading:*ISL 6.2, R4SL Chapter 24 - Regularization, R4SL Chapter 25 - Elastic Net*Deadline*: Homework 07 Due

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

**Monday**| 2017.11.6- Elastic net.
*R4SL:*Elastic Net

**Wednesday**| 2017.11.8*Overview:*Introduction to trees.*Reading:*ISL 8.1*ISL Slides:*Trees*Additonal Slides:*Part II: Tree-based Methods*ISL Videos:*Decision Trees, Pruning a Decision Tree, Classification Trees and Comparison with Linear Models

**Friday**| 2017.11.10- Discussed some finer details of
`R`

. *Deadline*: Homework 08 Due*Deadline*: Final Project Group Choice

- Discussed some finer details of

**Monday**| 2017.11.13- Continuation of tree discussion. Introduction to ensemble methods, mostly random forests.
*Reading:*ISL 8.2*R4SL:*Ensemble Methods*ISL Slides:*Trees*Additonal Slides:*Part I: Pruning, Bagging, Boosting*ISL Videos:*Bootstrap Aggregation (Bagging) and Random Forests, Boosting and Variable Importance

**Wednesday**| 2017.11.15- Continuation of tree discussion. Introduction to ensemble methods, mostly boosting.
*Reading:*ISL 8.2

**Friday**| 2017.11.17- 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?

- Extensions of random forests and boosting, in

**Monday**| 2017.11.13- No class. Fall break.
*Deadline*: Homework 09 Due

**Wednesday**| 2017.11.15- No class. Fall break.

**Friday**| 2017.11.17- No class. Fall break.

**Monday**| 2017.11.27**No class**. Consider a group meeting.

**Wednesday**| 2017.11.29- Unsupervised learning. Clustering.
*Reading:*ISL 10.1 - 10.3*R4SL:*Unsupervised Learning*ISL Slides:*Unsupervised Learning*Additional Slides:*Unsupervised Learning, Part I, Clustering*ISL Videos:*Unsupervised Learning and Principal Components Analysis, Exploring Principal Components Analysis and Proportion of Variance Explained, K-means Clustering, Hierarchical Clustering

**Friday**| 2017.12.01- Unsupervised learning. Clustering in
`R`

. *Reading:*ISL 10.1 - 10.3*Deadline*: Project proposals. No homework is due.

- Unsupervised learning. Clustering in

**Monday**| 2017.12.4- Unsupervised learning. PCA. Clustering again.
*Reading:*ISL 10.1 - 10.3*Additional Slides:*Unsupervised Learning, Part II, PCA

**Wednesday**| 2017.12.6**Friday**| 2017.12.01**No class.**Office hours 8 - 10 at David’s office. Work on projects!

**Monday**| 2017.12.11- Discussion of graduate student project results. Thoughts on keeping up to date with data science and machine learning.
*Reading:*Some Machine Learning and Data Science Resources

**Wednesday**| 2017.12.13**No class.**Office hours 8 - 10 at David’s office. Work on projects!*Deadline*: Homework 10 Due

**Friday**| 2017.12.15**No class.**Finals!

**T H U R S D A Y**| 2017.12.21*Deadline*: Final Project Report*Deadline*: Final Project Peer Review

**Group Choice**- Friday, November 10, 11:59 PM**Analysis Proposal**- Friday, December 1, 11:59 PM**Final Report**- Thursday, December 21, 10:00 PM**Peer Evaluation**- Thursday, December 21, 10:00 PM

**Autograder**- Saturday, December 9, 11:59 PM**Report**- Saturday, December 9, 11:59 PM