Introduction to artificial intelligence
Max amount of FITech students: 30
The course considers basic methods and problem solving with artificial intelligence-based approaches. The course is concentrated around concepts of search, regression, classification and clustering. The course highlights differences between supervised and unsupervised learning. The course also covers metrics to measure the performance of different artificial intelligence based methods.
- Brief introduction and history of artificial intelligence
- Search methods
- Supervised learning
- Data preprocessing
- Unsupervised learning
- Reinforcement learning
- Neural networks
After the course, the student will be able to
- identify potentially suitable artificial intelligence techniques for solving problems,
- distinguish between search, regression, classification and clustering type of problems,
- explain the use of supervised and unsupervised learning and performance measurement methods and metrics.
Lectures + lecture recordings, programming exercises and additional study material will be available. All material is distributed through learning platform on the course (moodle).
Python is used as a programming language on this course. All required Python libraries are provided through moodle.
The course is organized online.
Lectures and exercises will be held on Mondays and Tuesdays at 12-14 and on Thursdays at 10-12. The lecture recordings will be added to the Moodle platform for independant studies. Exams and submissions of independant exercises take place approximately every two weeks.
The course is completed through five weekly exams and five programming exercises. Weekly exams are online but require participation on announced times. There is no final exam.
More information in the University of Oulu study guide.
You can get a digital badge after completing this course.
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Further information about the studies
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