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http://amix.dk/blog/post/19574

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Explain how Hacker News ranking algorithm works and how you can reuse it in your own applications. It's a very simple ranking algorithm and works surprising well when you want to highlight hot or new stuff.

http://amix.dk/blog/post/19588

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This is a follow up post to How Hacker News ranking algorithm works. This time around I will examine how Reddit's default story and comment rankings work. Reddit's algorithms are fairly simple to understand and to implement and in this post I'll dig deeper into them.

The first part of this post will focus on story ranking, i.e. how are Reddit stories ranked? The second part of this post will focus on comment ranking, which does not use the same ranking as stories (unlike Hacker News), Reddit's comment ranking algorithm is quite interesting and the idea guy behind it is Randall Munroe (the author of xkcd).

http://alex.smola.org/teaching/berkeley2012/index.html

This link recently saved by newtonapple on February 28, 2012

Scalable Machine Learning occurs when Statistics, Systems, Machine Learning and Data Mining are combined into flexible, often nonparametric, and scalable techniques for analyzing large amounts of data at internet scale. This class aims to teach methods which are going to power the next generation of internet applications. The class will cover systems and processing paradigms, an introduction to statistical analysis, algorithms for data streams, generalized linear methods (logistic models, support vector machines, etc.), large scale convex optimization, kernels, graphical models and inference algorithms such as sampling and variational approximations, and explore/exploit mechanisms. Applications include social recommender systems, real time analytics, spam filtering, topic models, and document analysis.

http://en.wikipedia.org/wiki/Greek_alphabet

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http://en.wikipedia.org/wiki/Pointwise_mutual_information

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The PMI of a pair of outcomes x and y belonging to discrete random variables X and Y quantifies the discrepancy between the probability of their coincidence given their joint distribution and the probability of their coincidence given only their individual distributions, assuming independence.

The mutual information (MI) of the random variables X and Y is the expected value of the PMI over all possible outcomes.

The measure is symmetric (pmi(x;y) = pmi(y;x)). It can take positive or negative values, but is zero if X and Y are independent. PMI maximizes when X and Y are perfectly associated.

http://trentstrong.com/blog/2011/using-redis-sort-get-save-roundtrips.html

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http://blog.custora.com/2012/02/how-bayesian-probability-models-can-make-cl...

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http://bionicspirit.com/blog/2012/01/16/cosine-similarity-euclidean-distanc...

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http://www.keithschwarz.com/smoothsort/

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http://bionicspirit.com/blog/2012/02/09/howto-build-naive-bayes-classifier....

This link recently saved by newtonapple on February 27, 2012