GMU CS-584 Syllabus
Theory and Applications of Data Mining
Course Summary
The acceleration of technological advancements has led to the generation of an unprecedented volume of data. Data mining, a pivotal discipline at the intersection of computer science, statistics, and artificial intelligence, seeks to delineate insightful patterns from this extensive dataset. This domain not only encompasses applications such as credit rating and fraud detection but also extends to areas like genomics, climatology, quantum mechanics, and complex systems analysis. It emphasizes theoretical methodologies rooted deeply in statistical and computational models and explores the adaptability and versatility of these methods across diverse domains.
This course is structured to offer an in-depth exploration into the theoretical underpinnings of data mining techniques. Concurrently, it underscores their broad applications by providing case studies and hands-on coding sessions. Students will be equipped with both the knowledge and skills to navigate the realm of data mining.
Class Time and Location
Time: Tuesday 4:30 pm-7:10 pm
Date Range: Jan 16, 2024 - April 23, 2024
Location: Art and Design Building 2003
Instructor
Name: Keren Zhou
Email: kzhou6@gmu.edu
Office: ENGR 5315
Office Hour: Tuesday 3:00pm - 4:00pm
Teaching Assistants
Name: Long Cao Thanh Doan
Email: ldoan5@GMU.EDU
Office: ENGR 4456
Office Hour: Wednesday 2:00pm - 4:00pm
Piazza Moderator Hour: Monday - Friday
Prerequisites
Grade of C or better in CS 310 and STAT 344
Course Objectives
- To provide a comprehensive understanding of data mining techniques, elucidating both their potential and limitations.
- To instill proficiency in integrating data mining insights with software packages, with an emphasis on data analysis in Python.
- To facilitate hands-on experience, enabling students to conceptualize, design, and execute data mining projects.
Textbooks
- Data Mining: Concepts and Techniques, 3rd Edition (Optional)
It’s OK to use the fourth edition
- A Programmer's Guide to Data Mining (Optional)
Honor Code
Please follow GMU’s honor code policy:
To promote a stronger sense of mutual responsibility, respect, trust, and fairness among all members of the George Mason University community and with the desire for greater academic and personal achievement, we, the student members of the university community, have set forth this honor code: Student members of the George Mason University community pledge not to cheat, plagiarize, steal, or lie in matters related to academic work
And the CS department has its own policy.
Please do note there has been revisions to the honor code policy:
Unless permission to do so is granted by the instructor, you (or your group, if a group assignment) may not:
…
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use assistive technology, artificial intelligence, or other tools to complete assignments which can generate, translate, or otherwise create/correct code or answers (many types of assistive technology may be permitted, but you must ask permission)
Disability Accommodations
Should you possess documented evidence of a learning disability or any other condition that could impact your academic achievements, kindly ensure this documentation is registered with the Office of Disability Services. Subsequently, please initiate a conversation with the professor regarding potential accommodations.
Course Structure
Assignments
Throughout this course, students will complete up to three practical assignments. Each assignment must be done individually, while the project will be chosen and conducted in groups.
Further details about the assignments will be available on the Blackboard system.
- Grading for the Assignment will be split on
- Implementation (70%)
- Correctness (63%)
- Coding style (7%)
- Report (10%)
- Ranking results (20%)
- Top 10% (20%)
- Top 50% (16%)
- Rest (12%)
- Implementation (70%)
- A brief report should be submitted on Blackboard
- Code should be submitted on Gradescope
- 50% deduction if submitted on Blackboard
- e.g., 70% implementation → 35% implementation
- 50% deduction if submitted on Blackboard
Project
Central to this course is the semester project, closely tied to subjects broached during the lectures. Students have the autonomy to select their own project themes. Collaboration is encouraged, with projects typically involving teams of 2-4 participants. Piazza is available as a resource for sharing thoughts or for seeking team partners.
For project submissions, each group needs to make a single collective submission.
Project Inspiration
Explore interesting ideas on open source datasets or your own dataset
Project Components
- Project Proposal (2-5 pages in PDF, 20% of project grade): Your blueprint should encapsulate:
- The problem
- Anticipated hurdles
- Pertinent previous studies and their limitations
- Data sources to be utilized
- Evaluation methodology
Ensure all group member names are inscribed in the submitted document.
- Final Report and Code (5-10 pages PDF + CODE, 60% of project grade):
A. Report (30% of project grade): The format should mirror that of a scholarly article, encompassing at least:
- Abstract
- Introduction
- Data
- Methods
- Experiments
- Related Work
- Conclusions
- Division of Work
B. Code (30% of project grade): Nest your code within a "CODE" directory with a README file, including instructions to run the tests and validate results
Compile into a zip file, inclusive of the pdf and the CODE/ directory. Always inscribe all group member names in the pdf.
- Presentation (10 mins, 20% of the project grade): Presentations will be graded on the following aspects:
- Content depth and accuracy
- Clarity of presentation
- Ability to engage the audience and answer questions
- Time management
Grading
- Project (50%)
No late submission
- Assignments (30%)
A 3-days grace period is allowed for assignments
- Midterm Exam (20%)
No late submission
Schedule (subject to change)
Week | Date | Topic | Readings | Timeline |
---|---|---|---|---|
1 | Jan 16, 2024 | Introduction | Ch 1, 2.1 | |
2 | Jan 23, 2024 | Data Preprocessing & Python | Ch 2.2, 2.3 | |
3 | Jan 30, 2024 | Data Measurement | Ch 2.4 | HW1 out |
4 | Feb 6, 2024 | Classification-1 | Ch 3 | |
5 | Feb 13, 2024 | Classification-2 | Ch 4 | Proposal due |
6 | Feb 20, 2024 | Clustering-1 | Ch 7 | HW2 out |
7 | Feb 27, 2024 | Clustering-2 | Ch 8 | HW1 due |
9 | Mar 5, 2024 | Spring Recess | ||
8 | Mar 12, 2024 | Midterm Exam | HW3 out | |
10 | Mar 19, 2024 | Outlier Detection | Ch 9 | |
11 | Mar 26, 2024 | Pattern Mining-1 | Ch 5 | HW2 due |
12 | Apr 2, 2024 | Pattern Mining-2 | Ch 6 | |
13 | Apr 9, 2024 | Invited Talk | HW3 due | |
14 | Apr 16, 2024 | Project Presentation-1 | Final Report/Code Due | |
15 | Apr 23, 2024 | Project Presentation-2 |