Machine learning

Max amount of FITech students: 30

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

Machine learning principles are described in lectures and practical hands-on programming tasks are done on the online Matlab platform.

Course contents

• Introduction
• Mathematical optimisation for machine learning
• Linear and non-linear regression
• Two-class and multi-class classification
• Feature engineering and optimisation
• Model validation
• Kernel methods, neural networks, tree-based learners

Learning outcomes

After completing the course, the student

• can design and implement basic machine learning algorithms for regression and classification applications.
• can design and implement methods for optimising 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.

Teaching schedule

• Lectures on Tuesdays (Zoom, varying time)
• Weekly online lab work throughout the course on Wed (Zoom, varying time)
• Recordings will be available in Moodle
• This course has no exam

Completion methods

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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