BSc in Formatics Engineering or equivalent.
Theoretical classes with detailed presentation, using audiovisual means, of concepts, principles and fundamental theories and solving of basic practical exercises to illustrate the practical interest of the subject and exemplify its application to real cases. Theoretical-practical classes where the students solve practical exercises, which require the combination of different theoretical concepts and promote critical reasoning.The evaluation, which covers all the taught matters, clearly is focused on both the basic theoretical concepts and the ability to solve complex problems.
Pattern Recognition (PR) studies the design, development and implementation of systems that recognize patterns in data. This course teaches the algorithms that allow to explore applications of RP, formalizing them with analytic models of wide use in many fields. Basic concepts, models and tools for the understanding and design of a PRS. Through discussion involving the nature and difficulties inherent in a pattern classification problem, we discuss the discrimination of patterns, functions and decision regions, separability of classes and metrics. Study of extraction and feature selection, classification models parametric and non-parametric, dimensionality reduction and kernel methods, metrics for evaluating classifiers. Students should understand the phases of the PRS, choose the most appropriate classifier for solving a problem, complete their study based on the analysis of results and get knowledge from this analysis.
1. Pattern Discrimination: decision functions and decision regions; class separability metrics; Linear Discriminants (Euclidian and Mahalanobis), and Fisher discriminant.
2. Feature extraction and feature selection; feature ranking; Kruskal Wallis. Data pre-processing (outliers removal, normalization and scaling, missing data)
3. Clustering: Hierarchical and k-means algorithms
4. Parametric Methods: model selection, linear generalized models, mixture models, Bayes Classification, Parameter estimation: likelihood method; Bayes and risk estimation; Maxima A Posteriori (MAP); classifier; Kullback-Leibler divergence
5. Non-parametric methods: density estimation: Parzen windows and K-nearest neighbors.
6. Dimensionality reduction; Principal Component Analysis (PCA); Non-linear methods.
7. Kernel methods: Mercer kernel, Kernel PCA.
8. Classifier assessment sampling, confusion matrix and error probability; ROC curves; Bootstrapping, Boosting.
1. Bishop, C.M., ―Pattern Recognition and Machine Learning‖, Springer Verlag, 2006
2. Duda, R. O., Hart, P.E., and Stork, D.G., ―Pattern Classification,‖ 2nd ed. Wiley Interscience (2001)
3. J.P. Marques de Sá, ―Pattern Recognition: Concepts, Methods and Applications‖, 2001, XIX, 318 p., 197 illus., Springer-Verlag (2001)
4. M. N. Murty and V. S. Devi, ―Pattern Recognition: An Algorithmic Approach‖, Springer, 1st Edition., XII, 263 p. (2011)