Course - Artificial intelligence for robotics and automation - AIST2101
Artificial intelligence for robotics and automation
New from the academic year 2025/2026
About
About the course
Course content
The course provides a basic introduction to a selection of the following topics: optimal control and optimization, machine and deep learning, action selection and decision support, search and planning, estimation and smoothing, prediction and system identification. Methods are presented with emphasis on applications relevant to robotics and automation. Other topics may occur.
Learning outcome
Knowledge:
- The candidate can explain and compare theory, principles, applications, strengths and weaknesses of methods presented in the course
- The candidate has knowledge of how both traditional and state-of-the-art methods within artificial intelligence are applied in their field
Skills:
- The candidate can demonstrate the use of methods presented in the course, both through digital tools and simulation
General competence:
- The candidate can use digital tools to implement the methods presented in the course
- The candidate can explain the significance and challenges of artificial intelligence related to sustainable processes, services, or systems
- The candidate can present problems and relevant solution methods in a professional and scientific manner
- The candidate can discuss ethical challenges of artificial intelligence
Learning methods and activities
Learning activities generally include a mix of lectures, tutorials and practical lab/project work. A constructivist approach for learning is endorsed, with focus on problem solving and practical application of theory.
Compulsory assignments
- Assignments and laboratory exercises
Further on evaluation
The course has two evaluations with character grades: a collection of programming exercises and an individual exam. In order to pass the course, both evaluations must be passed.
A continuation exam is held in August for the written school exam, this may be change to an oral exam if there are few students. There is no continuation exam for the programming exercises, so they must be re-taken when the course is given ordinarily.
Students that want to improve their grade in the course, can choose to retake one of the two evaluations.
If the evaluation is changed, the whole course must be retaken.
Specific conditions
Admission to a programme of study is required:
Automation and Intelligent Systems - Engineering (BIAIS)
Recommended previous knowledge
IMAT1001 Mathematical methods 1, ISTT1002 Statistics, INGT1002 Programming, numerical mathematics and security, and AIST1001 Automation, introduction, or similar courses
Required previous knowledge
For applications for credentials, approval and incorporating of courses from previous semesters or other institutions' corresponding education programs, each application will be processed individually and the applicant should expect credit reductions for overlapping courses.
Course materials
Textbook is announced at the start of the semester.
Subject areas
- Engineering Cybernetics
- Engineering Subjects