Pattern Recognition

Higher education teachers: Dobrišek Simon

Subject description

Prerequisites:

Completed undergraduate study programme in the field of electrical engineering or related engineering or natural and mathematical sciences.
Enrolment in the 1st year of the Master’s study programme for Electrical Engineering (2nd cycle).
Basic knowledge of applied mathematics (vectors and matrices, eigenvectors and eigenvalues, some linear algebra, multivariate analysis, probability theory, and statistics).

Content (Syllabus outline):

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, probabilistic decision functions, learning probabilistic decision functions
Pattern classification by a multilayer perceptron: neural network topology, back-propagation training.
Testing pattern-recognition systems: methods for estimating the probability of the classification error with and without a test set.

Objectives and competences:

To provide students with an understanding of the basic mathematical and computational principles of constructing artificial perception systems, which are an essential part of intelligent systems in automation and control.

Intended learning outcomes:

• Knowledge and understanding:

After completing this course the student will be able to demonstrate a knowledge and understanding of the:

construction of intelligent systems based on pattern-recognition techniques ,
modelling of certain human mental capabilities (perception, cognition, learning),
pattern-feature extraction methods, clustering, classification and recognition.

• The use of knowledge:

The student will be able to use the acquired knowledge to construct technical systems that are able to symbolically describe their environment by watching, listening and sensing. Such systems are an essential part of all intelligent (robotic) systems, or are used as stand-alone products with high technological values. The student will be able to critically evaluate the consistency between the acquired knowledge and the application of the concepts of the pattern-recognition theory in practice.

• Transferable skills:

the use of literature and other resources in the fields of pattern recognition, machine learning and artificial intelligence;
the use of development tools and environments for computer programming (writing computer programs in different programming languages, such as C/C++, C#, Java, Python, or using the Matlab development environment);
problem solving: problem analysis, algorithm design, implementation and testing of a program.

Learning and teaching methods:

• lectures,
• laboratory exercises and projects,
• coursework.

Study materials

Readings:

1. S. Theodoridis, K. Koutroumbas: Pattern Recognition (4. izdaja), Academic Press, 2009.
2. C. M. Bishop: Pattern Recognition and Machine Learning, Springer, 2007