CSE6327

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  * 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