SGN-44006 Artificial Intelligence, 5 cr
Additional information
Suitable for postgraduate studies.
Person responsible
Tapio Elomaa
Lessons
| Implementation | Period | Person responsible | Requirements |
| SGN-44006 2019-01 | 3 |
Tapio Elomaa |
Accepted exercises and final exam |
Learning Outcomes
After completing the course the student will have an overview of different areas and techniques of artificial intelligence. In particular, the student is able to apply to problems arising in application fields the basic methods of solving problems by searching, informed search and exploration, inference in first-order logic, probabilistic reasoning, and reinforcement learning. Hands-on experience on artificial intelligence tools and techniques will be gathered in the weekly exercise sessions.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Logic, knowledge, and reasoning | propositional and first-order logic | knowledge bases |
| 2. | Problem solving and search | heuristic search | algorithm A* |
| 3. | Uncertain knowledge and reasoning | probabilistic reasoning | decision making |
| 4. | Machine learning | reinforcement learning | applications of reinforcement learning |
Instructions for students on how to achieve the learning outcomes
The course grade will be based on the course exam and weekly exercises. If the student demonstrates thorough understanding of the core content, s/he may pass the course with grade 3. In order to achieve grade 4, the student must also demonstrate competency in complementary knowledge. The student may achieve grade 5, if s/he also demonstrates good command of specialist knowledge. If there are minor shortcomings regarding the core content, the student may receive the grade 1 or 2, depending on the number of flaws. If there are significant shortcomings regarding core content, the student will not pass the course.
Assessment scale:
Numerical evaluation scale (0-5)
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Additional information | Examination material |
| Book | Artificial Intelligence A Modern Approach | Stuart J. Russell and Peter Norvig | 978-0-13-604259-4 | Main part of the lectures follows this extensive text book. | Yes |
Prerequisites
| Course | Mandatory/Advisable | Description |
| SGN-13006 Introduction to Pattern Recognition and Machine Learning | Mandatory |
Correspondence of content
There is no equivalence with any other courses