MAB608 Machine Learning 


ScheduleOrganizational Meeting(August 16) Overview of machine learning [slides odpslides pdf] [notes1.txt]Linear Regression and Review of Linear Algebra and Probability(August 18) Curve Fitting and Least Square Error [homework1.pdfinput] [slides odpslides pdf](August 23) Parameter Estimation I (Maximum Likelihood and Maximum Posterior) [slides odpslides pdf] (August 25) Parameter Estimation II (Fully Bayesian) and Decision Theory I [slides odpslides pdf] [notesdecisiontheory.pdf] Information Theory(August 30) Decision Theory II and Information Theory IDue September 13 [homework2.pdfinput2] [slides odpslides pdf] [notes5.txt] (September 1) Information Theory II (Applications) [paper about image alignmentslides pdf] Unsupervised LearningMarkov Chains and Google PageRank(September 6) Random Walks and Google PageRank I (Intro to Markov Chains) [slides 1 pdf] [notes7.txt](September 8) Random Walks and Google PageRank II (Steady State Solution) [slides 2 pdf] [notes8.txtexample1.m] (September 13) Random Walks and Google PageRank III (Transient Solution) [notes9.txtexample2.m] (September 15) Random Walks and Google Page Rank IV (Continous Time I) [notes10.txt] [BrowseRank paperUniformization paper] (September 20) Random Walks and Google Page Rank V (Continous Time II) [notes11.txt] Statistical ModelsClassical Models(September 22) Statistical Models I [slides pptslides pdf] [notes12.txt](September 27) Statistical Models II [likelihood2.pdf] [homework3.pdfdata3] Due October 13 (September 29) Statistical Models III, Stochastic Approximation and Matlab [slides pdfslides ppt] [Bonus homework on barycentric coordinates (Optional)] (October 4) No Class Today (JIC) (October 6) No Class Today (JIC) (October 11) No Class Today (GBR) Graphical Models(October 13) Bayesian Networks I: Graphical Models [slides pdf](October 18) Bayesian Networks II: Graphical Models  DSeparation [slides pdfalice.pdfMac Kay's book (chapter 16)] (October 20) Bayesian Networks III: Graphical Models  Markov Random Fields, Inference [slides pdfchater14a.pdf (Norvig and Russel)chapter14b.pdf (Norvig and Russel)see also chapter 16 of McKay's book] Further reading: Detecting Network Neutrality Violations with Causal Inference, M. Tariq, M. Motiwala, N. Feamster, M. Ammar To compute the confidence interval of measures obtained using sampling, see section 16.7.6 of Performance Evaluation material (in Portuguese) Homework 4 [input file: original file with spaces original file corrupted file corrupted file with spaces] (Question 1 due October 27, Question 2 due November 1) Supervised Learning
