# MATH 5392: Introduction to Data Mining

Fall 2017

## Table of Contents

## Announcement

Make sure that you install H2O and it is working before Oct 23rd lecture. Those who are unable to use H2O may need to install JDK 8. Please follow this link: http://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html.

`lab2.rmd.txt`

is uploaded for those who do not have a C compiler installed. Also, it can be knitted into a pdf file. You need to download or save this txt file (do **not** copy and paste).

### Final Presentation

- 11/30
- 2:00 - 2:30 DNO
- 2:35 - 2:55 Zach

- 12/5
- 2:00 - 2:20 UTA Mavericks
- 2:20 - 2:40 UTA 5392-5
- 2:40 - 3:00 UTA team 2

### Check the performance of R

In your R console, type in the following script:

install.packages("SuppDists"); source("https://r.research.att.com/benchmarks/R-benchmark-25.R")

To improve the performance, you can consider using the Microsoft R open. Find more information in https://mran.microsoft.com/documents/rro/installation.

## Course Syllabus

## Course Schedule

The instructor reserves the right to adjust this schedule in any way that serves the educational needs of the students enrolled in this course.

Week | Date | Topics | Reading | Notes |
---|---|---|---|---|

Week1 | 8/24 | Introduction | Lecture 1, Lab 1 | |

Week2 | 8/29 | R Markdown | Lecture 2, Lab 2, Lab 3 | |

Week2 | 8/31 | Review: linear algebra | Lecture 3, HW 1 - Due 9/12 | |

Week3 | 9/5 | Linear regression | Lab 4, Lab 5 | |

Week3 | 9/7 | Linear regression | ||

Week4 | 9/12 | Linear methods for classification | Lecture 4, HW 2 - Due 9/19 | |

Week4 | 9/14 | Linear methods for classification | Lab 6 | |

Week5 | 9/19 | Model assessment and selection | Lecture 5-I, HW 3 - Due 9/26 | |

Week5 | 9/21 | Elastic net regularization | Lecture 5-II, Lab 7 | |

Week6 | 9/26 | Splines, Generalized Additive models | Lecture 5-III, Lab 8, HW 4 - Due 10/3 | |

Week6 | 9/28 | Splines, Generalized Additive models | Lecture 6 | |

Week7 | 10/3 | Tree-based methods | Lab 9, HW 5 - Due 10/10 | |

Week7 | 10/5 | Tree-based methods | Lecture 7 | |

Week8 | 10/10 | Bagging and random forests | Lab 10, HW 6 - Due 10/17 | |

Week8 | 10/12 | Boosting | Lecture 8 | |

Week9 | 10/17 | Midterm exam | Midterm exam - Due 10/19 | |

Week9 | 10/19 | Final project guidelines, Imputation I | HW 7 - Due 10/26 | |

Week10 | 10/24 | Support vector machines | Lecture 9, Lab 11 | |

Week10 | 10/26 | Support vector machines | HW 8 - Due 11/2 | |

Week11 | 10/31 | Neural networks | Lecture 10 | |

Week11 | 11/2 | Neural networks | Lab 12, HW 9 - Due 11/9 | |

Week12 | 11/7 | Clustering | Lecture 11-I, Lecture 11-II | |

Week12 | 11/9 | Clustering | Lecture 11-III, HW 10 - Due 11/16 | |

Week13 | 11/14 | Group meeting (no class) |
Lecture 12-I, Lecture 12-II | |

Week13 | 11/16 | Multidimensional scaling | Lab 13, Final HW - Due 12/5 | |

Week14 | 11/21 | Bayesian networks | ||

Week14 | 11/23 | No class | Thanksgiving holidays | |

Week15 | 11/28 | Bayesian networks | ||

Week15 | 11/30 | Final presentations | ||

Week16 | 12/5 | Final presentations |