notes01_Linear Regression; Classi_cation and logistic regression; Generalized Linear Models.pdf
notes02_Generative Learning algorithms( Gaussian discriminant analysis; Naive Bayes Classifier).pdf
notes03_Support Vector Machines.pdf
notes04_Learning Theory.pdf
notes05_Regularization and model selection.pdf
notes06_The perceptron and large margin classifers.pdf
notes07a_The k-means clustering algorithm.pdf
notes07b_Mixtures of Gaussians and the EM algorithm.pdf
notes08_The EM algorithm.pdf
notes09_Factor analysis.pdf
notes10_Principal components analysis.pdf
notes11_Independent Components Analysis.pdf
notes12_Reinforcement Learning and Control.pdf