Students should have skills of programming with Python, and have followed and completed the bachelor's courses Data Mining and Artificial Intelligence.
Urban computing, as an interdisciplinary filed, is the science of using computing technology in solving urban challenges such as traffic, and pollution. Urban computing research also focuses on acquiring an understanding of the nature of urban phenomena, predict the future of cities, and plan their development. This course provides a comprehensive introduction to this topic. Throughout the course, students will get familiar with the necessary data acquisitions, integration, and modeling skills necessary for urban computing research.
Spatio-temporal data is the data collected as a result of movement of mobile objects, such as cars and people, over space. Spatio-temporal data mining is the core to most urban computing research projects. Most previously designed machine learning algorithms do not consider the specific characteristics of spatio-temporal data. In this course, we will talk about how different classes of machine learning algorithms have been adapted so far to perform on such data. We will also learn how additional data sources can be fused and integrated with spatio temporal data. The concepts throughout the course are explained using examples extracted from the state of the art urban computing research literature.
This course provides a comprehensive overview of the urban computing research. By the end of this course, students are expected to have:
- A clear understanding of spatio-temporal data mining approaches;
- Acquired necessary computational skills (modeling, integration, visualization) for realizing urban computing projects;
- The ability to criticize and assess the state of the art research and write a report paper based on it.
The most recent timetable can be found on the students' website.
Mode of instruction
- Interactive lectures
- Seminar presentations
The final score of the course is composed of:
- Active participation in class and discussions (10%)
- Assignments (25%)
- Presentation (15 %)
- Project (50%)
- Extra reading material composed of recent papers will become available through blackboard.
- You have to sign up for courses and exams (including retakes) in uSis. Check this link for information about how to register for courses.
- Please also register for the course in Blackboard as soon as the lecturer has made it available.