Max amount of FITech students: 30
In this course, the focus is especially on classifiers and classification. Other topics covered include statistical regression, Bayesian decision theory and feature extraction.
- Bayesian decision theory
- Parametric and non-parametric classification
- Feature extraction
- Classifier design and optimization
- Example classifiers
- Statistical regression methods
After completing the course, student can
- design simple optimal classifiers from the basic theory and assess their performance
- explain the Bayesian decision theory and apply it to derive minimum error classifiers and minimum cost classifiers
- apply regression techniques to practical machine learning problems.
Course is based on Duda RO, Hart PE, Stork DG: Pattern classification, John Wiley & Sons Inc., 2nd edition, 2001. There are also additional handouts.
face-to-face teaching, guided laboratory work and independent assignment. Laboratory work is supervised by assistants who also check that the task assignments are completed properly. The independent task assignment is graded. The course ends with a written exam.
Course contains face-to-face teaching, guided laboratory work and independent assignment. Both face-to-face and online possibilities within the course schedule. The laboratory works are done in an online system (Mathworks Grader). Student can do the lab works remotely or in the lab using the same online system. No exam.
You can get a digital badge after completing this course.
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