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Syllabus for

Academic year
EEN095 - Artificial intelligence and autonomous systems
Artificiell intelligens och autonoma system
Syllabus adopted 2021-02-15 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
Main field of study: Automation and Mechatronics Engineering

Teaching language: English
Application code: 67114
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 2,0 c Grading: UG   2,0 c    
0220 Examination 5,5 c Grading: TH   5,5 c   25 Oct 2021 am J,  04 Jan 2022 pm J,  22 Aug 2022 pm J

In programs

TIELL ELECTRICAL ENGINEERING - Common branch of study, Year 3 (compulsory elective)
TIMEL MECHATRONICS ENGINEERING, 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, SSY020 Linear systems and LEU236 Dynamical systems and control engineering, or equivalent knowledge. Furthermore, basic knowledge in Matlab is required for this course.


The course aims to provide a basic introduction to artificial intelligence based on machine learning. Particular emphasis is on applications within robotics.

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.
  • 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.
  • Simulation and testing of AI systems.


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


  1. Artificial Intelligence: A Modern Approach, S. Jonathan Russell, P. Norvig, Pearson.
  2. Machine Learning. T. M. Mitchell, McGraw-Hill.

Examination including compulsory elements

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

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.

Page manager Published: Mon 28 Nov 2016.