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--- lecture_notes [2016/10/11 19:07] – hj
+++ lecture_notes [2019/08/22 21:20] (current) – hj
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-  * Weeks 1-2: A. {{:ml1-intro.pdf|Machine Learning (basic concepts)}}; B. {{:ml2-math.pdf|Math foundation (updated a bit)}}: probabilities, Bayes theorem, statistics,  Entropy, mutual information,  decision tree,  optimization, matrix decomposition (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]])
+  * 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: {{: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 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: {{:ml4-decision-rule.pdf|Bayesian Decision Rule}}: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models
+  * Week 4: Bayesian Decision Rule: MAP decision rule; The Bayes Error; Statistical Data Modelling; Generative vs. Discriminative models
-  * Week 5-6: {{:ml5-discriminative.pdf|Discriminative Models}}: Statistical Learning theory; Perceptron; Linear Regression; Minimum Classification Error; Support Vector Machines (SVM); Ridge Regression; LASSO; Compressed Sensing
+  * 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