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Jupyter's nbviewerhttp://nbviewer.jupyter.org/
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-readings
Pattern Recognition and Machine Learninghttp://research.microsoft.com/en-us/um/people/cmbishop/prml/
Machine Learning: A Probabilistic Perspectivehttps://www.cs.ubc.ca/~murphyk/MLbook/
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-01--introduction-to-machine-learning
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-02--linear-algebra--optimization
nice online reviewhttp://www.juanklopper.com/opencourseware/mathematics-2/ipython-lecture-notes/
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-03--convex-functions--probability
Notebook Viewerhttp://nbviewer.jupyter.org/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture03_convex-functions-optimization/lecture03_Convex-Functions-and-Optimization.ipynb
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https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-04--linear-regression-part-i
Notebook Viewerhttp://nbviewer.jupyter.org/github/thejakeyboy/umich-eecs545-lectures/blob/master/lecture04_linear-regression-part1/lecture04_final_linear-regression-part-1.ipynb
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https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-05--linear-regression-part-ii
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https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-06--probabilistic-models--logistic-regression
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https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-07--linear-classifiers
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Generative Modelshttp://cs229.stanford.edu/notes/cs229-notes2.pdf
"The optimality of naive Bayes"http://www.cs.unb.ca/profs/hzhang/publications/FLAIRS04ZhangH.pdf
"On the optimality of the simple Bayesian classifier under zero-one loss"http://link.springer.com/article/10.1023/A:1007413511361
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-08--kernel-methods-i--kernels
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-09--kernel-methods-ii-duality--kernel-regression
Everything You Wanted to Know about the Kernel Trick (But were too Afraid to Ask)http://www.eric-kim.net/eric-kim-net/posts/1/kernel_trick.html
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-10--kernel-methods-iii-support-vector-machines--gaussians
Support Vector Machineshttp://cs229.stanford.edu/notes/cs229-notes3.pdf
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-11--kernel-methods-iii-bayesian-linear-regression--gaussian-processes
free onlinehttp://www.gaussianprocess.org/gpml/
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-12--machine-learning-advice
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-13--information-theory--exponential-families
Notes on Exponential Familieshttps://www.cs.princeton.edu/courses/archive/fall11/cos597C/lectures/exponential-families.pdf
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-14--probabilistic-graphical-models
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-15--latent-variables-d-separation-and-k-means
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-16--clustering--expectation-maximization
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-17--markov--hidden-markov-models
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-18--inference--applications-of-graphical-models
https://patch-diff.githubusercontent.com/usmandroid/EECS-545#lecture-19--principal-components-analysis--ica
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