CS 695 (Special Topic): Mining and Learning from Time Series Data

George Mason University

Department of Computer Science

CS 695: Mining and Learning from Time Series Data

Spring 2023

Professor Jessica Lin

Course Description: Time series is perhaps the most commonly encountered data type, encompassing almost every human endeavor including medicine, finance, aerospace, industry, science, etc. The unique characteristics of time series data present special challenges to researchers and practitioners. As a result, most data mining or machine learning algorithms cannot be applied directly on time series. This seminar provides an overview on state of the art research from the past few decades on mining and learning from time series data. Topics covered include data representation, similarity search, clustering, classification, anomaly detection, rule discoery, motif discovery, and visualization. Mining of streaming and spatiotemporal (trajectory) data will also be discussed.
Class Time and Location: Wednesday 4:30am-7:10pm
Horizon Hall 3010
Instructor: Dr. Jessica Lin
Email: jessica [AT] gmu [DOT] edu
Office Hours: Wednesday 3-4pm or by appointment
Teaching Assistant: TBA
Prerequisites

This course assumes knowledge in Data Mining (CS 584 or equivalent).

Course Format

The course will include lectures by the instructor, presentations from students, and class discussion. You will be asked to read research papers published in major conferences and/or journals (paper list TBA).

Grading: Grading will be based on participation, assignments, presentation, and a final project. Each week you are required to read two papers before class, and submit a paper review. Each student will give a 30-minute presentation on one paper in the semester. There will be a team project (no midterm oor final).; Assignments/paper review: 25%; Presentation: 15%; Project: 50%; Class participation/discussion: 10%

Project

There will be one team project in the semester. The project grade consists of project proposal (including project pitch, 10% total of overall course grade), progress report (5%), presentation (10%), and project report and code (25%).

Textbooks

Optional: Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin Kumar (click on the link for the companion website)

Paper List and Schedule

TBA

Honor Code Statement

The GMU Honor Code is in effect at all times. In addition, the CS Department has further honor code policies regarding programming projects, which are detailed here. Any deviation from the GMU or the CS department Honor Code is considered an Honor Code violation. unless otherwise specified.

Learning Disability Accommodation

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 then discuss with the professor about accommodations.

Tentative Schedule