Course aims:

Acquire knowledge about analysis and interpretation of data and information
Development of hardware and software applications for the field of intelligent systems and industrial informatics by choosing the optimal solution, designing a functional and integrated testing plan, interpreting the results, comparing them with the expected ones and developing the correction method
Processing of complex signals (voice, data, text, images), with encryption, compression
Modeling, implementing, testing, using and maintaining advanced electronic systems.
The applications aim to familiarize students with the general techniques of form recognition. Students must design (at least in Matlab) applications for the recognition of the following fields:  medical diagnosis;  automotive (pedestrian recognition, traffic signs, etc.);  biometrics;

Course Entry Requirements:

Artificial Intelligence, machines and shape recognition

Course contents:

1. Introduction to the theory of machine tools. The learning process. -Learning in neural networks - Assessing hypotheses and Bayesian learning - Instance-based learning. - Machines with vector support 2.Neural networks. Feed-forward neural networks (with supervised learning). -Multilayer Perceptron (MLP); - Networks with basic radial functions (FBR);Recurrent neural networks (with supervised learning). - The Hopfield Network; - Two-way associative memory (BAM); Competitive neural networks (with unsupervised learning). - Self-Organizing Maps (SOM); - Networks based on adaptive resonance theory (ART); 4. Fuzzy and Neuro-Fuzzy systems - Introduction to nuanced logic ("fuzzy"). "Fuzzy" relationships. The metric "fuzzy". "Fuzzy" implications. Approximate reasoning. Classification systems with "fuzzy" rules. - Neuro-fuzzy networks. Integration of "fuzzy" logic and neural networks. Fuzzy neurons. 5. Genetic algorithms. - The stages of a genetic algorithm: selection, crossing, mutation. - Neural networks with genetic algorithms. - Applications in shape recognition 6. Two-dimensional signal processing elements (images) 7. Recognition of shapes in images - Statistical methods. Bayes classifier, - Classification based on prototype, - The kNN classifier, 8. Applications. - Classification of medical signals and images. - Classification of planar objects. - Biometric technology, iris recognition, face identification. - Artificial view mobile robots

Teaching methods:

lectures, examples, exercises

Learning outcomes:

Development of programs in a general and / or specific programming language, starting from the specification of the requirements and until the execution, debugging and interpretation of the results.

Learning outcomes verification and assessment criteria:

projects

Recommended reading:

Mitchell, T, Machine Learning, The McGraw-Hill Companies, 1997,
FRANK Y. SHIH, IMAGE PROCESSING AND PATTERN RECOGNITION Fundamentals and Techniques, Published by John Wiley & Sons, Inc., Hoboken,, New Jersey., 2010,
Richard G Lyons, Understanding Digital Processing, Pearson Education, Inc., 2011,
Steven W Smith, Digital Signals Processing, California Technical Publishing, 1999,