SGN-44006 Artificial Intelligence, 5 cr

Additional information

Suitable for postgraduate studies.

Person responsible

Tapio Elomaa


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 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:

Completion parts must belong to the same implementation

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   


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

Updated by: Kunnari Jaana, 05.03.2019