Kuva: Reaktor

A massive open online course

Take our hugely popular AI course!

What is artificial intelligence? How does it affect your work and life? How will artificial intelligence develop? The course aims to make AI more understandable. You do not need an in-depth knowledge of maths or programming skills to take the course. You can start the course flexibly in the MOOC learning environment and complete it at your convenience. For further information, please see the course description.

ALSO NOTE: Are you wondering what it takes to complete the course? Click here for frequently asked questions (e.g., information on course assessment, duration and credits). You can also ask questions (e.g., about course assignments) in the course chat room .

Ilmoittautuminen ja opintomaksu

You will be given instructions for course registration in the MOOC online learning environment after completing the course.


  • Please note that the course registration form is open for a pre-defined period only (31th of August 2020)
  • If you wish to have the ECTS entered in the University of Helsinki’s student records, you must register for the course at the Open University.
  • Course materials are available for everyone without officially registering on the course.
  • If you enter erroneous information when registering, we cannot register your credits.
  • Students and international students at the University of Helsinki can enrol on the course with their University of Helsinki username.
  • If you do not have a Finnish personal identity code, please contact the University of Helsinki Admission Services in order to register for the course.
  • Credits for July course completions will be registered in August.

The course credits will be registered within 4 to 6 weeks of registration.

Practical instructions for studying
Ar­range­ments for stu­dents in need of spe­cial sup­port

Open University reserves the right to make changes to the study programme.


Optional course in the Bachelor Programme of Computer Science

  • suitable for all students in any study programme
  • the target audience is especially students with little or no computer science studies

If you are completing DEFA (Digital Education For All) studies, Elements of AI is one of the courses that can be completed for application purposes; please see information in Finnish.

No formal prerequisites beyond high-school mathematics (basic arithmetics with fractions)

No formal prerequisites beyond high-school mathematics (basic arithmetics with fractions)

After the course, if the student wishes to continue learning about AI, we recommend learning some programming and taking introductory AI courses. These courses are mainly held at the faculty:

  • a follow-up MOOC "AI Programming" will begin in Spring 2019
  • DATA15001 Introduction to AI is a closely related intermediate course that also includes programming exercises on the same topics
  • DATA11001 Introduction to Data Science (advanced course)
  • DATA11002 Introduction to Machine Learning (advanced course)
  • closely related Bachelor programmes include the Bachelor of Science and Bachelor of Computer Science
  • closely related Master's programmes include the Master of Data Science and Master of Computer Science

After completing the course, the student will be able to:

  • Identify autonomy and adaptivity as key concepts of AI
  • Distinguish between realistic and unrealistic AI (science fiction vs. real life)
  • Express the basic philosophical problems related to AI including the implications of the Turing test and Chinese room thought experiment
  • Formulate a real-world problem as a search problem
  • Formulate a simple game (such as tic-tac-toe) as a game tree
  • Use the minimax principle to find optimal moves in a limited-size game tree
  • Express probabilities in terms of natural frequencies
  • Apply the Bayes rule to infer risks in simple scenarios
  • Explain the base-rate fallacy and avoid it by applying Bayesian reasoning
  • Explain why machine learning techniques are used
  • Distinguish between unsupervised and supervised machine learning scenarios
  • Explain the principles of three supervised classification methods: the nearest neighbor method, linear regression, and logistic regression
  • Explain what a neural network is and where they are being successfully used
  • Understand the technical methods that underpin neural networks
  • Understand the difficulty in predicting the future and be able to better evaluate the claims made about AI
  • Identify some of the major societal implications of AI including algorithmic bias, AI-generated content, privacy, and work
  • any stage of studies
  • the course is offered continuously
  • the course can be started at any time, and completed at any pace
  • recommended duration is six weeks

What is AI?

  • motivation
  • definition of AI
  • philosophy of AI

AI problem solving

  • formulating and solving problems using state diagrams
  • formulating simple games (tic-tac-toe or chess) as game trees
  • solving game trees using the minimax algorithm

Real world AI

  • expressing uncertainty using probability
  • probabilities and odds
  • Bayes formula

Machine learning

  • nearest neighbor classifier
  • linear regression
  • logistic regression

Neural networks

  • concepts of neural computation
  • learning in neural networks
  • perceptron classifier


  • public perception of AI
  • critical evaluation of claims made about AI (e.g., singularity, AI winter)
  • societal implications and ethics of AI

Exercises including multiple choice quizzes, numerical exercises, and questions that require a written answer

The multiple choice and numerical exercises are automatically checked

The exercises with written answers are reviewed by other students (peer grading) and in some cases by the instructors

The course material is available at https://course.elementsofai.com/

  • The course material contains text and interactive elements
  • The exercises are designed to challenge the student to re-read the material and access further sources enough to be able to produce an answer
  • Successful completion requires 90% completed exercises and minimum 50% correctness
  • Only one attempt is allowed in the multiple choice quizzes and numerical exercises
  • Exercises with written answers are accepted or rejected based on the reviews: in case of rejection, another attempt is allowed (as many times as required)

Taking this course does not give students access to a University of Helsinki user account. If you want to view and share information about your studies after completing the course, sign up for the Oma Opintopolku -service maintained by the Finnish National Agency for Education. The ECTS will be displayed within two days in the Oma Opintopolku -service after credits have been registered to the University of Helsinki. To register for the Oma Opintopolku – service you must identify yourself by using Finnish bank identification codes, mobile certificate or certificate card.

Questions regarding the course:

The course is a MOOC (Massive Open Online Course) course available for everyone free of charge. You can start the course flexibly and complete it at your convenience. You can study the course independently in the online learning environment of the course at https://www.elementsofai.com/

Teemu Roos. The Elements of AI course is created by Reaktor and the University of Helsinki.

The course is part of the subject studies in Computer Science.