PA196: Pattern Recognition Exercises: Linear discriminants - part I Dr. Vlad Popovici popovici@iba.muni.cz Institute of Biostatistics and Analyses Masaryk University, Brno Vlad PA196: Pattern Recognition 1. Perceptron 1 simple implementation of perceptron with discussion 2 discussion on the sklearn.linear_model.Perceptron To do: generate a binary classification problem (see source code) check the help for the Perceptron class use fit method to build a model use the predict method to predict the labels of a data set shuffle (numpy.shuffle()) the data and repeat the training: is there any difference? randomly partition (have a look at random.choice()) partition your data into a training set and a validation set train a new model and test it on the validation set Vlad PA196: Pattern Recognition 2. Fisher Discriminant FDA is LDA for 2 classes check sklearn.lda.LDA class go through the code http://scikit-learn.org/stable/ _downloads/plot_lda_qda.py repeat the training/testing steps from perceptron, with the FDA classifier Vlad PA196: Pattern Recognition