Pattern Recognition

• Introduction to pattern recognition: basic concepts and terminology, pattern representation, computational complexity of pattern-recognition algorithms, the main types of pattern-recognition methods.
• Pattern segmentation: speech-signal segmentation techniques and image segmentation techniques
• Heuristic features of patterns: features of speech segments, features of image segments.
• Application domain analysis using clustering techniques: definition of clusters and clustering, pattern-similarity measures, pre-processing of sets of patterns, hierarchical clustering algorithm.
• Optimal feature generation: class-separation measure, feature selection and feature extraction, feature generation using orthogonal transformations.
• Pattern classification by pattern matching: pattern template matching, k-nearest-neighbour rule.
• Decision-based pattern classification: decision functions, designs of pattern classifiers, polynomial decision functions, training algorithms, support vector machines, probabilistic decision functions, learning probabilistic decision functions.
• Pattern classification by neural networks: neural network topologies, back-propagation training, deep neural networks, recurrent neural networks.
• Testing pattern-recognition systems: methods for estimating the probability of the classification error with and without a test set.

The objective of the course is to provide the student with the knowledge of the basic mathematical and computer concepts that are used in the construction of artificial perception systems and are essential components of intelligent systems in automation. The acquired knowledge forms the basis for understanding and designing automatic pattern recognition systems as well as the artificial intelligent systems that are based on automatic learning and knowledge acquisition from different environmental sensor data.

The lectures provide a theoretical background of all the considered models and methods together with simple computational examples that illustrate the key characteristics of all the presented methods. A textbook and other study material, such as lecture notes with solved example problems and lecture slides, are available to the students. As part of the lectures, the students receive optional homework assignments including theoretical questions as well as computational exercises that enable the students to promptly verify the acquired knowledge. Practical work is carried out as part of the laboratory exercises, where the students solve given programming problems. As part of the laboratory exercises, the students also carry out additional elective projects within which the selected method of automatic pattern recognition in the selected field of application should be implemented. The results of the elective projects are reported in written reports.