5 ECTS credits /135 hours of work
|Language of instruction
Lectures and main material in Finnish. English material available.
Period IV in spring semester. Recommended completion year: first or second.
Upon completion the student the student will have the elementary skills to identify the potentially applicable artificial intelligence techniques for solving problems. He/she is able to recognize search, regression, classification, and clustering problems, and to explain the use of supervised and nonsupervised learning, performance measurements and metrics.
- Introduction to artificial intelligence
- Search methods
- Supervised learning
- Data preprocessing
- Unsupervised learning
- Reinforcement learning
- Neural networks
|Mode of delivery
Face-to-face teaching. Online learning option available.
|Learning activities and teaching methods
Lectures 42h / group work 70 h / self-study 23 h. The exercises are completed as group work in multi-disciplinary teams.
All Bachelor level students.
|Prerequisites and co-requisites
No prerequisites. Python programming skills are highly recommended.
|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
All course material (lectures and exercises) are available in course Moodle space.
|Assessment methods and criteria
The course utilizes continuous assesment. During the course there are five intermediate exams which will be used in final evaluation. The course also includes five group exercises of which at least four need to be passed.
The course utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail.
|Working life cooperation
Experts from industry are invited to present real world artificial intelligence solutions.
Course learning environment is in Moodle moodle.oulu.fi.