Syllabus
Classification task, feature- and syntax- based object description.
Feature selection and preprocessing.
Classifiers, basic definitions.
Bayesian decision theory, discriminant functions, separating hypersurfaces, minimum error criterion.
Decision trees.
Discriminant analysis, linear classifier.
Support Vector Machines (SVM).
Neural nets.
Unsupervised learning.
Hidden Markov models.
Classification quality evaluation.
Syntactic pattern recognition, grammatical inference. Special grammar types.
Annotation
The course is focused on basic machine learning algorithms used in computer vision tasks.
Practical part takes place in a computer lab equipped with Matlab.