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521160P Introduction to Artificial Intelligence, 5 ECTS cr 
Code 521160P  Validity 01.08.2017 -
Name Introduction to Artificial Intelligence  Abbreviation IntrArtInt 
Scope5 ECTS cr   
TypeBasic Studies Discipline4307 Information Engineering 
  Grading1 - 5, pass, fail 
Unit Computer Science and Engineering DP 

Riku Hietaniemi 

ECTS Credits 

5 ECTS credits / 135 hours of work

Language of instruction 

English. Material is also available in Finnish.


Spring semester, period IV.

Learning outcomes 

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
  • Regression
  • Classification
  • 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.

Target group 

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.

Person responsible 

Riku Hietaniemi

Working life cooperation 

Experts from industry are invited to present real world artificial intelligence solutions.

Other information 

The course uses Moodle learning environment (


Current and future instruction
Functions Name Type ECTS cr Teacher Schedule
registration period has not begun Introduction to Artificial Intelligence  Course  Riku Hietaniemi  14.03.22 -03.05.22

Future examinations
No examinations in WebOodi