Admission requirements
Recommended prior knowledge
Programming experience with Python;
Machine learning and data mining;
Deep Learning (have already taken the course or taking it parallel);
Statistics.
Description
Urban computing, as an interdisciplinary field, is the science of using computing technology in solving urban and environmental challenges such as traffic, and pollution. This course provides a comprehensive introduction to this topic. Throughout the course, students will get familiar with data acquisitions, integration, and modeling skills required 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 designed 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 research literature. Students will put the knowledge of spatio-temporal data mining they acquire in this course into practice, using different assignments and a final project. Topics to be addressed are:
Acquiring data for urban computing research
Processing time-series data
Processing spatial data
Processing spatio-temporal data
Visualization techniques for spatio-temporal data
Machine learning algorithms for urban computing research
Deep learning for urban computing research
Course objectives
This course aims at providing knowledge for students to design machine learning algorithms for spatio-temporal data. While reviewing the theoretical concepts we greatly focus on how environmental and urban applications can be addressed with such methods. Notably, we discuss and see examples of various data sources (Earth observations, GPS sensors, open data), their imperfections and peculiarities and design algorithmic solutions for them. After this course students are able to:
categorize and compare existing machine learning approaches for handling spatio-temporal and time-series data;
collect and employ existing spatio-temporal data, evaluate and analyze their properties and imperfections;
identify new research problems based on spatio-temporal data sources and design a methodology to address it;
develop new algorithms tailored to spatio-temporal data and design an evaluation approach based on the properties of the available data;
present and communicate their result results (i.e., the relevance of the research questions, methods, evaluation and conclusions).
Timetable
The most recent timetable can be found at the Computer Science (MSc) student website.
In MyTimetable, you can find all course and programme schedules, allowing you to create your personal timetable. Activities for which you have enrolled via MyStudyMap will automatically appear in your timetable.
Additionally, you can easily link MyTimetable to a calendar app on your phone, and schedule changes will be automatically updated in your calendar. You can also choose to receive email notifications about schedule changes. You can enable notifications in Settings after logging in.
Questions? Watch the video, read the instructions, or contact the ISSC helpdesk.
Note: Joint Degree students from Leiden/Delft need to combine information from both the Leiden and Delft MyTimetables to see a complete schedule. This video explains how to do it.
Mode of instruction
Interactive lectures
Seminar presentations
Assignments
Paper
Presentation
Course load
Total hours of study: 168 hrs. (= 6 EC)
Lecture hours (lectures + presentations): 24 hrs.
Practical assignments: 40 hrs.
Final project: 104 hrs.
Assessment method
The final score of the course is composed of:
Assignments (35%)
Presentation (15 %)
Project (40%)
Project peer review (10%)
It is possible to resit assignments and the project. However, the resit of assignments gets a maximum grade of 7. To pass the course the course combined grade, project grade and each assignments grade should be over 5.5. No resit option is available for presentations. Submitting a project proposal is compulsory (no submission implies deduction of 20% of the project grade).
Reading list
- Extra reading material composed of recent papers will become available through Brightspace.
Registration
As a student, you are responsible for enrolling on time through MyStudyMap.
In this short video, you can see step-by-step how to enrol for courses in MyStudyMap.
Extensive information about the operation of MyStudyMap can be found here.
There are two enrolment periods per year:
Enrolment for the fall opens in July
Enrolment for the spring opens in December
See this page for more information about deadlines and enrolling for courses and exams.
Note:
It is mandatory to enrol for all activities of a course that you are going to follow.
Your enrolment is only complete when you submit your course planning in the ‘Ready for enrolment’ tab by clicking ‘Send’.
Not being enrolled for an exam/resit means that you are not allowed to participate in the exam/resit.
Contact
Lecturer: dr. M. Baratchi
Remarks
Software
Starting from the 2024/2025 academic year, the Faculty of Science will use the software distribution platform Academic Software. Through this platform, you can access the software needed for specific courses in your studies. For some software, your laptop must meet certain system requirements, which will be specified with the software. It is important to install the software before the start of the course. More information about the laptop requirements can be found on the student website.