Course syllabus for Artificial intelligence and autonomous systems

The course syllabus contains changes
See changes

Course syllabus adopted 2020-02-19 by Head of Programme (or corresponding).

Overview

  • Swedish nameArtificiell intelligens och autonoma system
  • CodeEEN095
  • Credits7.5 Credits
  • OwnerTIMEL
  • Education cycleFirst-cycle
  • Main field of studyAutomation and Mechatronics Engineering
  • DepartmentELECTRICAL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 67126
  • Maximum participants80
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0120 Laboratory 2 c
Grading: UG		2 c
0220 Examination 5.5 c
Grading: TH		5.5 c
  • 26 Okt 2020 am J
  • 05 Jan 2021 pm J
  • 23 Aug 2021 pm J

In programmes

Examiner

Go to coursepage (Opens in new tab)

Eligibility

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.

Aim

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

Content

  • 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.

Organisation

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

Literature

Will be decided later

Examination including compulsory elements

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

The course syllabus contains changes

  • Changes to course rounds:
    • 2020-06-26: Examinator Examinator changed from Bengt Lennartson (l) to Emmanuel Dean (deane) by Viceprefekt
      [Course round 1]
  • Changes to examination:
    • 2020-09-30: Grade raising No longer grade raising by GRULG
    • 2020-09-30: Grade raising No longer grade raising by GRULG