Math and Probability for Data Science
Esp. for Reinforcement Learning
Probability: cov(x,y): https://math.stackexchange.com/questions/1581773/computing-the-covariance-of-flipping-a-coin-in-the-first-and-last-flips
Fundamental Concept in Machine Learning
https://faculty.washington.edu/tamre/BayesTheorem.pdf
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Lecture Slides – old book
https://www.inf.ed.ac.uk/teaching/courses/rl/lecturelist.html
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Color :
https://digitalsynopsis.com/design/color-thesaurus-correct-names-of-shades/
Random Sum:
http://www.math.unl.edu/~sdunbar1/ProbabilityTheory/Lessons/Conditionals/RandomSums/randsum.shtml
Simple probability examples:
http://jwilson.coe.uga.edu/emt668/emat6680.folders/dickerson/probability/titled.html
https://faculty.math.illinois.edu/~kkirkpat/SampleSpace.pdf
Series and Sum:
http://www.mathcentre.ac.uk/resources/uploaded/mc-ty-convergence-2009-1.pdf
Reference:
http://www.sjsu.edu/faculty/watkins/runs.htm
https://mathinsight.org/image/two_dice_distribution
http://www.mathguide.com/lessons2/ExpectedValues.html
http://www.mathguide.com/lessons2/ExpectedValues.html
https://www.texasgateway.org/resource/44-geometric-distribution-optional
https://www.me.utexas.edu/~jensen/ORMM/computation/unit/rvadd/discrete_dist/geometric.html
http://www.mathguide.com/lessons2/ExpectedValues.html
http://www.sjsu.edu/faculty/watkins/runs.htm From: https://sitestree.com/math-and-probability-for-data-science/
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Post Data:2019-05-03 11:45:31
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