5 ECTS credits / 135 hours of work
|Language of instruction
English. Material is also available in Finnish.
Spring semester, period IV.
Upon completion the student
- will have the elementary skills to identify the potentially applicable artificial intelligence techniques for solving problems.
- He/she can recognize search, regression, classification, and clustering problems, and to
- explain the use of supervised and non-supervised learning, performance measurements and metrics.
- Introduction to artificial intelligence
- Search methods
- Supervised learning
- Data pre-processing
- Unsupervised learning
- Reinforcement learning
- Neural networks
|Mode of delivery
Face-to-face teaching. Online learning option available.
The compulsory weekly exams for the course are organised in Moodle automatically on a scheduled basis. They must be completed according to the schedule presented at the beginning of the course on a specific day within a specific time window. Otherwise, in the independent completion of the course, it is possible to adapt the studies flexibly according to your own schedule.
|Learning activities and teaching methods
Lectures 42h, exercise work 70 h and self-study 23 h. The exercises can be completed individually or as group work in multi-disciplinary teams.
All Bachelor level students and 1st year Master level students.
|Prerequisites and co-requisites
No prerequisites. Python programming skills are highly recommended such as 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
All course material is available in course Moodle space.
|Assessment methods and criteria
The course utilizes continuous assessment. During the course there are five intermediate exams which will be used in final evaluation. The course also includes five exercises of which at least four need to be passed. These exercises can be completed individually or in groups.
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.
The course uses Moodle learning environment (moodle.oulu.fi).