# CCO - 740 - Pattern Recognition

Total of Credits: 8

Hours for Theoretical Classes: 60

Hours for Exercises or Seminars: 60

## Objective

- To provide the students with the mathematical tools for the development of pattern recognition and machine learning systems. At the end of the course, the student should be able to:
- design and code linear or non-linear supervised classifiers from a broad set of paradigms (Bayes decision theory, Methods based on artificial neurons, Support Vector Machines, Logistic regression, among other non-parametric approaches);
- design and code unsupervised classifiers to find clusters in multivariate data (hierarquical clustering, k-means, Gaussian mixture models and the Expectation-Maximization algorithm);
- design and code feature extraction methods for dimensionality reduction (Principal Component Analysis, Linear Discriminant Analysis, Kernel PCA);
- assess the supervised classification performance using quantitative metrics (confusion matrix, accuracy, Kappa coefficient)

## Catalog Description

- Supervised classification: Perceptron, Adaline (Widrow-Hoff), Logistic regression, Bayes Decision Theory, Bayesian classifier under Gaussian hypothesis, minimum distance classifier, K-nearest neighbors, support vector machines.
- Unsupervised classification: Hierarquical clustering, the k-means algorithm, Gaussian mixture models and the Expectation-Maximization algorithm.
- Feature extraction
- Linear methods for dimensionality reduction: Principal component analysis (PCA), Linear discriminant analysis (LDA), Non-negative matrix factorization (NMF)
- Non-linear Kernel based methods: Kernel PCA
- Performance evaluation metrics: confusion matrix, accuracy, precision and recall, F1-score, Cohen's Kappa coefficient. Error/Accuracy estimation methods: holdout, resubstitution, k-fold cross validation and leave-one-out cross validation.
- Advanced topics and new trends: Manifold learning, neural networks, backpropagation, deep learning, metric learning, etc.

## Bibliography

- Richard O. Duda, Peter E. Hart, David G. Stork, Pattern Classification, 2nd Edition, Wiley-Interscience, 2000 (disponível na BCO).
- Andrew R. Webb, Keith D. Copsey, Statistical Pattern Recognition, 3rd Edition, Wiley, 2011 (disponível na BCO).
- Sergios Theodoridis, Konstantinos Koutroumbas, Pattern Recognition, 4th Edition, Academic Press, 2008 (disponível na BCO).
- Keinosuke Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Edition, Academic Press, 2013 (disponível na BCO).
- Geoff Dougherty, Pattern Recognition and Classification: An Introduction, Springer, 2013.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition, Springer, 2016.
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011.
- Jürgen Schürmann, Pattern Classification: A Unified View of Statistical and Neural Approaches, 1st Edition, Wiley-Interscience, 1996.