Course syllabus adopted 2021-02-15 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 67116
- Maximum participants80 (at least 10% of the seats are reserved for exchange students)
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0120 Laboratory 2 c Grading: UG | 2 c | 0 c | 0 c | 0 c | 0 c | 0 c | |
| 0220 Examination 5.5 c Grading: TH | 5.5 c | 0 c | 0 c | 0 c | 0 c | 0 c |
|
In programmes
- TIELL - ELECTRICAL ENGINEERING - Common branch of study, Year 3 (compulsory elective)
- TIMEL - MECHATRONICS ENGINEERING, Year 3 (compulsory elective)
Examiner
- Emmanuel Dean
- Senior Researcher, Systems and Control, Electrical Engineering
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, 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
- Artificial Intelligence: A Modern Approach, S. Jonathan Russell, P. Norvig, Pearson.
- 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.