4 classification i k nearest neighbours naive bayes

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4. Classification I : k-nearest neighbours, Naive Bayes, ROI Curve, AUC. 5. Model Evaluation and Selection : Receiver operating characteristic curve (ROC). Cross Validation methods for model selection. 6. Classification II : LDA, QDA. Logistic Regression, Multivariate Logistic Regression 7. Classification III : Feature Selection, Applications in Text processing and Image recognition. 8. Generalized Linear Models : Poisson and Gamma Regression, applications in supervised learning. 9. Unsupervised Learning : (a) Dimensionality Reduction and Clustering algorithms: PCA, k-means. (b) The EM Algorithm : applications include clustering via normal and Bernoulli mixture models 10. SVMs : classification and regression using Support Vector Machines, kernel methods and the kernel “trick”. (a) Support Vector Machines : geometrical interpretation, primal problem. (b) Dual Problem, Kernel trick , non separable problems. 11. Neural Networks and Deep Learning : (a) Neural Networks and Back-propagation. (b) Introduction to Tensor Flow and optimization (c) Convolutional Neural Networks with applications to image processing (d) Recurrent Neural Networks with applications to Natural Language Process- ing 3
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