Max amount of FITech students: 30
Machine learning principles are described in lectures and practical hands-on programming tasks are done on the online Matlab platform.
- Mathematical optimization for machine learning
- Linear and non-linear regression
- Two-class and multi-class classification
- Feature engineering and optimization
- Model validation
- Kernel methods, neural networks, tree-based learners
After completing the course, student
- can design and implement basic machine learning algorithms for regression and classification applications.
- can design and implement methods for optimizing cost functions for machine learning tasks.
- can apply the most common methods for machine learning.
Course material and platforms
- Jeremy Watt, Reza Borhani, AggelosK. Katsaggelos: Machine Learning Refined (Foundations, Algorithms, and Applications), 2nd edition, Cambridge University Press, 2020.
- Matlab tutorials
- Lecture slides
MathWorks Grader platform. Registrations are arranged in the beginning of course.
- Lectures on Mondays at 12-14 (through Zoom, recording available in Moodle)
- Weekly online lab work throughout the course
Lectures and partially guided lab works. Each laboratory assignment is evaluated automatically by the MathWorks Grader giving feedback to students to improve their solutions. The final grade for the course is calculated by the teacher from the completed assignments at the end of course. The number of successfully completed assignments affects the course grade. An indicated number of subtasks must be completed in each week to pass the course. No final exam arranged.
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
Contact person for applications