Course syllabus

Course syllabus for Artificial intelligence and autonomous systems

Course syllabus adopted 2025-02-20 by Head of Programme (or corresponding).

Overview

Swedish name:
Artificiell intelligens och autonoma system
Code:
EEN096
Credits:
7.5 Credits
Owner:
TIMEL
Education cycle:
First-cycle
Main field of study:
Automation and Mecha­tron­ics Engineering
Department:
ELECTRICAL ENGINEERING
Grading:
TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language

    English

  • Application code

    67124

  • Maximum participants

    80 (at least 10% of the seats are reserved for exchange students)

  • Block schedule

  • Open for exchange students

    Yes

Credit distribution

Module
Sp1
Sp2
Sp3
Sp4
Summer
Not Sp
Examination dates
0125 Laboratory 2 c
Grading: UG		2 c
0225 Project 5.5 c
Grading: TH		5.5 c

In programmes

Examiner

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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

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

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.

Aim

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.

Content

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

Organisation

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

Literature

  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 final project 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 about disability study support.

Artificial intelligence and autonomous systems | Chalmers