* Weeks 1-2: A. {{:ml1-intro.pdf|Machine Learning (basic concepts)}}; B. {{:ml2-math.pdf|Math foundation}}: probabilities and statistics, Bayes theorem,   Entropy, mutual information,  decision tree,  optimization,  matrix factorization (Weekly Reading: [[http://www.cse.yorku.ca/course_archive/2011-12/W/6328/Duda_AppMath.pdf|W2]])    (A useful online manual on [[http://www.ee.ic.ac.uk/hp/staff/dmb/matrix/calculus.html|Matrix Calculus]])

  * Week 3: {{:ml3-feature.pdf|Feature Extraction in Machine Learning}}: PCA, LDA, manifold learning (MDS, SNE, LLE, Isomap), data virtualization. (Weakly reading: {{:tutorial-pca.pdf|a tutorial paper on PCA}}, {{:ml-blackart.pdf|a paper on ML}}). 

  * Week 4-5: {{:ml5-discriminative.pdf|Discriminative Models}}: Statistical Learning theory; Perceptron; Linear Regression; Minimum Classification Error; Support Vector Machines (SVM); Ridge Regression; LASSO; Compressed Sensing

  * Week 6: {{:ml6-ann.pdf|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: [[http://sebastianruder.com/optimizing-gradient-descent/|other gradient descent algorithms for deep learning]]).

  * Week 7: {{:ml4-decision-rule.pdf|Bayesian Decision Rule}}: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models

  * Week 8: {{:ml7-generative.pdf|Generative Models and Parameter Estimation}} : generative models in general; maximum likelihood estimation; Bayesian Learning; Gaussian, logistic regression, Multinomial, Markov chains, GMM. 

  * Week 9: {{:ml8-graphicalmodel.pdf|Graphical models}}: Bayesian Networks vs Markov random fields;  Conditional independence; Inference in graphical models; belief propagation; variational inference