Autonomous vehicles – planning, control, and learning systems
Currently there is an enormous interest for autonomous vehicles both within academia and industry, with applications for example in self-driving cars and trucks, and unmanned aerial vehicles. The course “Autonomous vehicles — planning, control, and learning systems“, at LiU, provides the theoretical and technological basis for how such systems work.
Machine Learning, Systems and Control – Master’s Programme
The 2-year international master programme “Machine laerning, systems and control” is a result of collaboration between the departments of Mathematics, Automatic Control, Computer Science and Electrical and Information Technology at Lund University. The programme integrates knowledge built by ELLIIT researchers.
Ph.D. Course on Motion Planning and Control
Erik Frisk and Björn Olofsson are responsible for the doctoral course “Motion Planning and Control“, held at the Division of Vehicular Systems, LiU. The course is open for all Ph.D. students as well as senior undergraduate students, and covers both fundamental algorithms and state-of-the-art methods for motion planning and control.
Data science and machine intelligence specialization
A specialization based largely upon experiences and results from ELLIIT, in “Datadriven science and machine intelligence “, is available to students within engineering physics and electrical engineering at Linköping University. Read more on this website.
Complex networks and big data
This 6 credit master-level course at Linköping University deals with the way objects are connected. These objects may be computers or routers in the Internet, webpages in the World Wide Web, people in social networks, or cities in a transportation network, for example. Read more on this website.
Foundations of Machine Learning
The course “Foundations of Machine Learning” (opens in new window) is a fully web-based self-study course, created by ELLIIT Faculty member Fredrik Lindsten and offered by Linköping University. It teaches the underlying theory of machine learning by litterature reading and get hands-on experience by solving exercises on the course’s web platform.