Weeks 1-2: A. Machine Learning (basic concepts); B. Math foundation (updated a bit): probabilities, Bayes theorem, statistics, Entropy, mutual information, decision tree, optimization, matrix decomposition (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

Week 5-6: Discriminative Models (updated): Statistical Learning theory; Perceptron; Linear Regression; Minimum Classification Error; Support Vector Machines (SVM); Ridge Regression; LASSO; Compressed Sensing

Week 7: Artificial Neural Networks and Deep Learning: artificial neural networks (model structure and learning criteria); error back-propagation algorithm; fine-tuning tricks; Convolutional neural networks; Recurrent neural networks (Extra reading assignment: other gradient descent algorithms for deep learning).

Week 8: Generative Models and Parameter Estimation: generative models in general; maximum likelihood estimation; Bayesian Learning; Gaussian, logistic regression, Multinomial, Markov chains, GMM.

Week 9: Graphical models: Bayesian Networks vs Markov random fields; Conditional independence; Inference in graphical models; belief propagation; variational inference