Fall 2022: Data Mining [CS584]

Professor: Carlotta Domeniconi, Rm 4424 ENG, carlotta\AT\cs.gmu.edu. Office hours: TBA
Teaching Assistant: Pooya Fayyazanavi, Rm 4456, Office hours: TBA
Prerequisites: CS310 and STAT344 (C or better in both).
Students should be familiar with basic probability and statistics concepts, and linear algebra. Programming experience in Python preferred.
Java or C will work as well, but the assignments will use the Python framework. Please expect lots of programming in all the assignments.
Location and Time: We meet in Horizon Hall 2016, T 7:20pm - 10:00pm
Textbook (required): P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Pearson. Book's companion website
Link to the up-to-date Syllabus and to the Schedule of Classes

General Description and Preliminary List of Topics

Data mining is the process of automatically discovering useful information in large data repositories. The course covers key concepts and algorithms at the core of data mining.

Topics include: classification, clustering, association analysis, anomaly detection.

Grading

Assignments: 40%
Midterm + Final: 55% (highest score counts 30%; lowest score counts 25%)
Class activities and participation: 5%
Extra credit: competition winners for homework

Exams: All exams are closed-book, and they must be taken at the scheduled time and place, unless prior arrangement has been made with the instructor. Missed exams cannot be made up.

Assignments: There will be 4-5 competition-style programming assignments. The preferred programming language is Python. Competition winners will get 1% extra credit added to the final grade. No late submission will be accepted.

Honor Code Statement

The GMU Honor Code is in effect at all times. In addition, the CS department has its own Honor Code policies regarding programming assignments. Any deviation from the GMU or the CS department Honor Code is considered a Honor Code violation.

Disabilities

If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and come talk to me about accommodations.