Weeks 1-2: A. Machine Learning (basic concepts); B. Math foundation: probabilities and statistics, Bayes theorem, Entropy, mutual information, decision tree, optimization, matrix factorization (Weekly Reading: W2) (A useful online manual on Matrix Calculus)
Week 3: Feature Extraction in Machine Learning PCA, LDA, manifold learning (MDS, SNE, LLE, Isomap), data virtualization. (Weakly reading: a tutorial paper on PCA, a paper on ML).
Week 4: Bayesian Decision Rule: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models