Artificial Intelligence — the revolution hasn’t happened yet by Michael Jordan [outside article]


A thoughtful article by one of the leading machine learning researchers on whether we can call “machine learning” “artificial intelligence”.

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying the use of the phrase. But this is not the classical case of the public not understanding the scientists β€” here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us β€” enthralling us and frightening us in equal measure. And, unfortunately, it distracts us.

Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville [book]


The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The complete version of the book including lecture materials is available online for free.

Towards thearetical understanding of deep learning by Sanjeev Arora [outside article]


A presentation on Deep Learning including a brief history and tutorial.

Which test to use in what situation [outside article]


Having trouble deciding what statistical test to use for your data? Use this handy flowchart from Penn State to decide. It includes a review of all the statistical techniques provided, as well as a table consisting of inferences, parameters, statistics, types of data, examples, analysis, Minitab commands, and conditions.

American Statistical Association’s statement on p-values [outside article]


The American Statistical Association (ASA) has released a β€œStatement on Statistical Significance
and P-Values” with six principles underlying the proper use and interpretation of the p-value. The ASA
releases this guidance on p-values to improve the conduct and interpretation of quantitative
science and inform the growing emphasis on reproducibility of science research. The statement
also notes that the increased quantification of scientific research and a proliferation of large,
complex data sets has expanded the scope for statistics and the importance of appropriately
chosen techniques, properly conducted analyses, and correct interpretation.

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