Studied notions and techniques

Linear Models for Regression - Linear Basis Function Models - Bayesian Linear Regression - Linear Models for Classification - Discriminant Functions - Least squares for classification - Fisher’s linear discriminant - Fisher’s discriminant for multiple classes - The perceptron algorithm - Probabilistic Generative Models - Probabilistic Discriminative Models - Logistic regression - Multiclass logistic regression - Model comparison and BIC - Mixture Models and EM - Mixtures of Gaussians - Relation to K-means - Principal Component Analysis - PCA for high-dimensional data - Probabilistic PCA - EM algorithm for PCA - Kernel PCA - Independent component analysis - Modelling nonlinear manifolds - Multidimensional Scaling - Nonlinear Dimension Reduction and Local Multidimensional Scaling.