Basics of AI and machine learning

Artificial intelligence and machine learning are renewing our ways of working. The methods have developed rapidly in recent years, and soon almost anyone will be able to utilise artificial intelligence in their work. However, taking advantage of new opportunities requires knowledge of basic methods.

Machine learning is a specific area of artificial intelligence. Its function is to enhance a software to work better based on ground knowledge and user actions.

During these courses, you will be familiarised with AI and machine learning. Course contents are for example

  • Problem solving using artificial intelligence based approaches
  • Concepts of search, regression, classification and clustering types of problems
  • Difference between supervised and unsupervised learning

You will get a good overview on several intelligent approaches which can be used to solve different types of real life tasks.

NB! Some courses have limits on the amount of FITech students. Persons without a valid study right to a Finnish university have preference to those courses.

AI and machine learning courses:

= Contact learning
= Online learning
= Blended learning (online & contact learning)
Aalto University: Machine learning with Python (CS-EJ32101), 2 ECTS. 9.9.–13.12.2019.

On this online course, we will introduce some of the most widely used ML methods such as regression, classification, feature learning and clustering.

We will discuss these methods in a hands-on fashion using coding assignments which include implementations of ML methods using the programming language Python.

Responsible teacher: Alex Jung (alex.jung(at)aalto.fi)

University of Oulu: Johdatus tekoälyyn (521160P), 5 ECTS. 9.3.–8.5.2020.

The course is organised in Finnish! Small part of the course material in English.

Max amount of FITech students: 30

No prerequisites, but basics of Python recommended.

Course content:

  • Problem solving using artificial intelligence based approaches.
  • Concepts of search, regression, classification and clustering types of problems.
  • Difference between supervised and unsupervised learning.

The course also covers metrics to measure performance of selected methods.

During the courses the students get a good overview on several intelligent approaches which can be used to solve different types of real life tasks.

More info >> 

Responsible teacher: Olli Silvén (olli.silven(at)oulu.fi)

Further information

University of Oulu:

Coordinator Riku Hietaniemi (riku.hietaniemi(at)oulu.fi)

Aalto University:

Coordinator Minna Kivihalme (minna.kivihalme(at)aalto.fi)

Type of study unit

Set of courses

Teaching semester

2019–2020

Host university

Aalto University, University of Oulu

Open for degree student

Yes

Open for non-student

Yes

Level of studies

Basic

Teaching methods

Online or contact learning

Place of contact learning

Oulu

Language

English and Finnish

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