# Data 311: Machine Learning

## Welcome

All of the course material can also be accessed through Canvas. The syllabus can be found in the *Syllabus* tab in the Navigation bar.

## Lectures

Lectures will be uploaded here. Quarto includes a built in version of the reveal.js-menu plugin. You can access the navigation menu using the button located in the bottom left corner of the presentation^{1}. Clicking the button opens a slide navigation menu that enables you to easily jump to any slide.

### Print/Save to PDF:

Reveal presentations can be exported to PDF via a special print stylesheet.

- Toggle into Print View using the
`E`

key (or using the Navigation Menu) - Open the in-browser
^{2}print dialog (CTRL/CMD+P). - Change the
**Destination**setting to**Save as PDF**. - Change the
**Layout**to**Landscape**. - Change the
**Margins**to**None**. - Enable the
**Background graphics**option. - Click
**Save**ðŸŽ‰

### Schedule

Lecture | Topic | Supplementary Reading |
---|---|---|

1 | Welcome! Introduction To R and RStudio | |

2 | Notation and Terminology | ISLR Ch 1 |

3 | Assessing Regression Models -MSE and Testing vs. Training MSE |
ISLR 2.2.1, 2.2.2 |

4 | Linear Regression | ISLR Section 3.1, 3.2 |

5 | Extensions to the linear regression model: Interaction, Categorical Predictors, Polynomial regression. KNN Regression (non-parametric approach) |
ILSR Section 3.3, 3.4, 3.5, Lab 3.6 |

6 | Logistic Regression | ISLR Section 4.1, 4.2, 4.3 |

7 | Assessing Classification Models | ILSR Section 2.2.3 |

8 | Classification models: Bayes Classifier, KNN Classification and Discriminant Analysis | ILSR Sections 2.2.3 and 4.4.1, 2, 3 |

9 | Distance measures: Euclidean Distance, Manhattan Distance, Mahalanobis Distance, Matching Binary Distance, Asymmetric Binary Distance, Gowerâ€™s Distance | Ch 3 of MSR^{3} |

10 | Hierarchical Clustering and \(k\) - means clustering | ISLR 12.4.1, 12.4.2 and 12.4.3 |

11 | Cross Validation | ILSR 5.1 |

12 | Bootstrap | ILSR 5.2, 5.3 |

13 | Classification and Regression trees | ISLR Chapter 8.1 |

14 | Bagging and Random Forests | ISLR Chapter 8.2.1, 8.2.2^{4} |

15 | Boosting | ISLR 8.2.3 |

16 | Ridge Regression and the LASSO | ISLR 6.1, 6.2 |

17 | Dimensionality reduction with PCA | ILSR 12.2 |

Midterm 2 Session | ||

18 | PCA regression and PLS | ISLR 6.3.1, 6.3.2 |

19 | Gaussian Mixture Models (GMM) | (see slides for references) |

20 | Neural Networks | ISLR 10.1, 10.2 |

### Lab Schedule

Lab | Topic |
---|---|

1 | An Introduction to R and R markdown |

2 | Assessing Regression Models. This will require you to download this clock auction data set. (see Lab 3.6 of ISLR for more examples) |

3 | Make predictions, analyze diagnostic plots, identify potential problems in multiple linear regression, and compare multiple regression models using the test MSE |

4 | Logistic Regression and Classification Simulation |

5 | LDA/QDA and classification metrics |

6 | Hierarcical and k-means clustering |

7 | Cross-validation and Bootstraping |

8 | Tree-based methods |

9 | Ridge Regression/LASSO and PCA |

10 | PCAreg, PLS and Neural Nets |

## Footnotes

You can also open the navigation menu by pressing the

`M`

key.â†©ï¸ŽNote: This feature has only been confirmed to work in Google Chrome and Chromium.â†©ï¸Ž

Multivariate Statistics with R by Paul J. Hewsonâ†©ï¸Ž

For more details see

*Random Forests with R*by Robin Genuer, Jean-Michel Poggi (2020)â†©ï¸Ž