5 ECTS credits / 135 hours of work
|Language of instruction
The course in held in the spring semester, during period III. For bachelor students of Computer Science and Engineering specializing to artificial intelligence, it is recommended to complete the course at the 3rd spring semester.
After completing the course, students
- know the basic search strategies that can be applied in problem solving and optimization.
- understand how search-based decisions are made in game-like competitive applications.
- know the basic principles of probabilistic reasoning in artificial intelligence systems.
- know how rational decision making under uncertainty can be formulated using utility theory.
- understand the fundamentals of machine learning and how some of the established methods can be applied to problems in AI.
- are familiar with advanced AI applications of perception and robotics and how probabilistic inference and machine learning can be used in these settings.
In the course projects, students get some experience in programming and using search methods.
intelligent agent types, uninformed search methods, informed (heuristic) search, local search, constraint satisfaction problems, adversarial search, uncertainty handling, probabilistic reasoning, utility, machine learning, decision networks, Markov decision process, reinforcement learning, applications
|Mode of delivery
The tuition is implemented as face-to-face and web-based teaching. Moodle environment is used in the course.
|Learning activities and teaching methods
Lectures 28 h / Group work (programming projects) 42 h / Self-study 65 h
The primary target group is the students of the Computer Science and Engineering specializing in Artificial Intelligence.
|Prerequisites and co-requisites
Completion of the course "521160P Introduction to Artificial Intelligence" (lectured in Finnish) is recommended, but is not a prerequisite. It is also recommended that a student has completed studies related to probability and statistics (e.g. course "031021P Probability and Mathematical Statistics") and Python programming (e.g. course "521141P Elementary Programming").
|Recommended optional programme components
The course is an independent entity and does not require additional studies carried out at the same time.
|Recommended or required reading
The course is based on the book Stuart Russell, Peter Norvig (2010, global edition 2016): Artificial Intelligence: A Modern Approach (3rd Edition), Chapters 1-6, 13-18, 20-21, partly 24-25.
The course utilizes materials of an introductory course on artificial intelligence taught at UC Berkeley ( target=_blank>http://ai.berkeley.edu).
|Assessment methods and criteria
The assessment of the course is based on the final exam. Both the final exam and the course projects must be passed. Well-done course projects can increase the grade by one unit.
The course utilizes a numerical grading scale 0-5. In the numerical scale zero stands for a fail.
Pekka Sangi, Jaakko Suutala
|Working life cooperation
The course does not contain working life cooperation.
Course work space can be found from University of Oulu Moodle platform moodle.oulu.fi.
Moodle page in Spring 2021 will be target=_blank>https://moodle.oulu.fi/course/view.php?id=3211