5 ECTS credits.
|Language of instruction
The course unit is held in the spring semester, during period III. It is recommended to complete the course at the end of studies.
After completing the course, student
- can design simple optimal classifiers from the basic theory and assess their performance.
- can explain the Bayesian decision theory and apply it to derive minimum error classifiers and minimum cost classifiers.
- can apply regression techniques to practical machine learning problems.
Introduction. Bayesian decision theory. Parametric and non-parametric classification. Feature extraction. Classifier design and optimization. Example classifiers. Statistical regression methods.
|Mode of delivery
Face-to-face teaching, guided laboratory work and independent assignment. The laboratory works are done on an online system (Mathworks Grader). Student can do the lab works remotely or in the lab using the same online system.
|Learning activities and teaching methods
Lectures 16 h, Laboratory work 16 h, and Self-study the rest (Independent task assignment).
Students who are interested in machine learning and pattern recognition theory and methods.
|Prerequisites and co-requisites
The mathematic studies of the candidate degree program of computer science and engineering, or equivalent. Programming skills, especially basics of the Matlab.
|Recommended optional programme components
The course is an independent entity and does not require additional studies carried out at the same time.
|Recommended or required reading
Will be informed when the course starts.
|Assessment methods and criteria
Laboratory work is supervised by assistants who also verify that the task assignments are completed properly. The Matworks Grader online system also verifies the completed tasks. The independent task assignment is graded which establishes the grade for the course.
Read more about assessment criteria at the University of Oulu webpage.
The course unit utilizes a numerical grading scale 1-5. In the numerical scale zero stands for a fail. The final grade is established by the independent task assignment.
|Working life cooperation