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--- lecture_notes [2019/11/04 15:54] – hj
+++ lecture_notes [2019/11/13 16:42] (current) – hj
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   * 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]])
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   * 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: Graphical models: Bayesian Networks vs Markov random fields;  Conditional independence; Inference in graphical models; belief propagation; variational inference
+  * Week 9: {{:ml8-graphicalmodel.pdf|Graphical models}}: Bayesian Networks vs Markov random fields;  Conditional independence; Inference in graphical models; belief propagation; variational inference