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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
EEN095 - Artificial intelligence and autonomous systems  
Artificiell intelligens och autonoma system
Syllabus adopted 2020-02-19 by Head of Programme (or corresponding)
Owner: TIMEL
7,5 Credits
Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Education cycle: First-cycle
Major subject: Automation and Mechatronics Engineering

Teaching language: English
Application code: 67126
Open for exchange students: Yes
Block schedule: C
Maximum participants: 80

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0120 Laboratory 2,0c Grading: UG   2,0c    
0220 Examination 5,5c Grading: TH   5,5c   26 Oct 2020 am J   05 Jan 2021 pm J,  23 Aug 2021 pm J

In programs

TIELL ELECTRICAL ENGINEERING - Common branch of study, Year 3 (compulsory elective)


Emmanuel Dean

  Go to Course Homepage


General entry requirements for bachelor's level (first cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

The same as for the programme that owns the course.
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

The courses MVE580 Linear algebra and differential equations, LEU432 Introduction to computer engineering and LEU236 Dynamical systems and control engineering, or equivalent knowledge.


The course aims to provide a basic introduction to artificial intelligence, including both planning and machine learning. Particular emphasis is on applications within robotics and self-driving vehicles.

Learning outcomes (after completion of the course the student should be able to)

  • describe the basic principles in artificial intelligence (AI), including both learning and decision making.
  • apply learning methods on autonomous systems, especially for robot path planning.
  • analyze and apply learning techniques based on system identification.
  • combine learning and decision making for both continuous and discrete systems


  • AI planning based on finite state machines.
  • Model-free reinforcement learning.
  • System identification based on least square estimation.
  • Model-based learning for control.
  • Simulation and testing of control systems.


The course comprises lectures, guest lectures, exercises, and home assignments. At guest lectures and booked sessions for home assignments attendance is compulsory.


Will be decided later

Examination including compulsory elements

Passed written exam and approved home assignemnts are required for pass grade on the entire course.

Page manager Published: Thu 04 Feb 2021.