Model-based Machine Learning (42186)
Model-based Machine Learning leverages the powerful framework of Probabilistic Graphical Models (PGMs) and recent developments in probabilistic programming to allow the combination of domain knowledge with data driven methods in a very simple way.
Data Science for Mobility (42184)
This course introduces the portfolio of tasks and techniques necessary for applying Data Sciences to mobility problems. It includes an introduction to Python programming, data wrangling, problem formulation, Spatial statistics, and the basic suite of machine learning and spatial data processing algorithms.
Projects for Mobility in the Smart City (42183)
The course introduces modern transportation technologies for monitoring, controlling and planning, as well as traveler side tools, such as advanced traveler information systems, crowdsourced apps and the internet, in the context of the “Smart City” vision. Having these in mind, student groups will design projects to solve challenges proposed by course instructors and invited lectures (e.g. from City of Copenhagen, Movia, etc.).
Intelligent Transport Systems (42891)
The course provides the students with an overview of the technological, systems and institutional aspects of intelligent transport systems (ITS). The students will be able to understand the importance of ITS and make use of today’s and tomorrow’s technology to achieve an efficient and safe utilization of the infrastructure and fleet, for public, commercial and individual multimodal transport.