Akond Rahman, PhD arahman@tntech.edu Foundation Hall, Room#132 Office hours: 9:30 AM – 10:30 AM , Friday
Recommended Textbook: Introduction to Data Mining, Tan, Steinbach, and Kumar, second edition (https://www-users.cs.umn.edu/~kumar001/dmbook/index.php)
| Date | Tentative Schedule |
|---|---|
| Jan 21 | Introduction, Team Formation |
| Jan 23 | Data types, Statistics |
| Jan 28 | Data Pre-processing |
| Jan 30 | Text Mining |
| Feb 04 | Association Rule Mining |
| Feb 06 | Association Rule Mining |
| Feb 11 | Sequence Mining |
| Feb 13 | Sequence Mining, Time Series Analysis |
| Feb 18 | Project Presentation |
| Feb 20 | Exam#1 |
| Feb 25 | Guest lecture - Dr. Chudamanai (Hypothesis Testing) |
| Feb 27 | Graph Mining |
| Mar 03 | Graph Mining |
| Mar 05 | Graph Mining |
| Mar 10 | Project Presentation |
| Mar 12 | Guest lecture - Dr. Chudamani (Markov Chains) |
| Mar 24 | Spring Break |
| Mar 26 | Spring Break |
| Mar 31 | Graph Mining |
| Apr 02 | Project Presentation |
| Apr 07 | Qualitative Data Analysis |
| Apr 09 | Exam#2 (Take Home) |
| Apr 14 | Supervised Learning |
| Apr 16 | Supervised Learning |
| Apr 21 | Project Presentation, Extra credit distributed |
| Apr 23 | Machine Learning, Take home exam distributed |
| Apr 28 | Clustering |
| Apr 30 | Clustering , Take home exam due |
| May 05 | Extra credit due , Project Report Due |
| May 06 | Tentative grades distributed |
- Exam#1: 25%
- Exam#2: 20%
- Exam#3: 15% (Take home)
- Project: 40%
- Some extra credit
- Final Report: 30%
- Mandatory sections: Introduction, Research Questions, Methodology, Findings, References => 25%
- Report must be in Latex => 25%
- Report must be free of typos, grammaticall errors, and passive voices => 25%
- Report must discuss who did what part of the project => 25%
- Code: 30%
- GitHub Activity-Commits, Issue discussions: 20%
- Elevator pitches or pechakucha presentations: 20%
- Each project member will give updates in front of the class
- 5-10 minutes per person for each group
- Round robin fashion
- A: 90-100
- B: 80-89
- C: 70–79
- D: 60–79
- F: less than 59
- All exams are open book, one page both side handwritten cheat sheet allowed, Cheat sheets need to be submitted with exam scripts.
- No questions on source code in exams.
- Project source code must be maintained in Gitlab/GitHub repos.
- If the instructor detects copy-paste in source code or exams then that will result in direct F for the course .
- Each project update will include updates so far as a Markdown file which will reside in the repo. Instructions on how to run the program in the Markdown file. The required libraries needed to run code should be written.
- Final project report should be spell-checked, typo-free, without passive voice.
- Mismatch between reported output and source code results will be inspected. The instructor will download repos, install libraries, and run the code based on the instruction provided in the mentioned Markdown file. For reproducibility teams are allowed to use Docker containers.
- Every regrade request is due within 48 hours.
- One project report per team.