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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 
Olli Silven 

ECTS Credits 

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.

Learning outcomes 

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
  • Regression
  • Classification
  • 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.

Target group 

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.

Person responsible 

Riku Hietaniemi

Working life cooperation 

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

Other information 

Course learning environment is in Moodle


Current and future instruction
Functions Name Type ECTS cr Teacher Schedule
registration period has not begun Introduction to Artificial Intelligence  Course  Riku Hietaniemi  15.03.21 -04.05.21

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