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    <dc:creator>ramhiser</dc:creator><description><![CDATA[An excellent paper from Jerome Friedman (1997) intended to stimulate debate regarding how the Statistics discipline should respond to what we now call Big Data.]]></description>
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<dc:subject>slides regularized-discriminant-analysis sparse-discriminant-analysis high-dimensional multivariate classification machine-learning</dc:subject>
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<dc:identifier>https://pinboard.in/u:ramhiser/b:fd9165c26a4b/</dc:identifier>
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</item>
<item rdf:about="http://www.economist.com/blogs/babbage/2011/11/artificial-intelligence">
    <title>Artificial intelligence: Difference Engine: Luddite legacy | The Economist</title>
    <dc:date>2013-05-06T03:04:25+00:00</dc:date>
    <link>http://www.economist.com/blogs/babbage/2011/11/artificial-intelligence</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>artificial-intelligence future work-force machine-intelligence machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:e21a53172127/</dc:identifier>
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<item rdf:about="http://www.technologyreview.com/featuredstory/513696/deep-learning/">
    <title>New Techniques from Google and Ray Kurzweil Are Taking Artificial Intelligence to Another Level | MIT Technology Review</title>
    <dc:date>2013-04-28T19:26:25+00:00</dc:date>
    <link>http://www.technologyreview.com/featuredstory/513696/deep-learning/</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>machine-learning deep-learning artificial-intelligence machine-intelligence ray-kurzweil google</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:fed9a1deaded/</dc:identifier>
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<item rdf:about="http://normaldeviate.wordpress.com/2013/04/13/data-science-the-end-of-statistics/">
    <title>Data Science: The End of Statistics? « Normal Deviate</title>
    <dc:date>2013-04-17T23:26:57+00:00</dc:date>
    <link>http://normaldeviate.wordpress.com/2013/04/13/data-science-the-end-of-statistics/</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>data-science statistics machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:bf6a18592860/</dc:identifier>
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</item>
<item rdf:about="http://webdesign.maratz.com/lab/responsivetypography/realtime/">
    <title>Responsive Typography Demo</title>
    <dc:date>2013-02-11T18:59:55+00:00</dc:date>
    <link>http://webdesign.maratz.com/lab/responsivetypography/realtime/</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[Face detection on web camera.]]></description>
<dc:subject>face-detection machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:a5937e6019b8/</dc:identifier>
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</item>
<item rdf:about="http://mro.massey.ac.nz/handle/10179/2798">
    <title>Some aspects of covariance regularisation in discriminant analysis : a thesis presented in fulfilment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, New Zealand</title>
    <dc:date>2013-01-22T21:55:44+00:00</dc:date>
    <link>http://mro.massey.ac.nz/handle/10179/2798</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[Appended to the end of the dissertation are several papers published by the student. One paper in particular estimates the RDA parameters via the Bhattacharyya distance for the two population case -- this approach has a huge speed advantage and gives comparable results to the model selection method initially proposed.]]></description>
<dc:subject>regularized-discriminant-analysis machine-learning classification covariance-matrices</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:f6f8c126ff70/</dc:identifier>
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</item>
<item rdf:about="http://www.image-net.org/">
    <title>ImageNet</title>
    <dc:date>2013-01-02T21:59:59+00:00</dc:date>
    <link>http://www.image-net.org/</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. Currently we have an average of over five hundred images per node. We hope ImageNet will become a useful resource for researchers, educators, students and all of you who share our passion for pictures. ]]></description>
<dc:subject>machine-learning datasets image-recognition big-data</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:00bcdd77c8fc/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:big-data"/>
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<item rdf:about="http://www-stat.stanford.edu/~owen/courses/305/Rudyregularization.pdf">
    <title>Slides: Regularization: Ridge Regression and the LASSO</title>
    <dc:date>2013-01-02T19:56:13+00:00</dc:date>
    <link>http://www-stat.stanford.edu/~owen/courses/305/Rudyregularization.pdf</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[Excellent set of notes on ridge regression, LASSO, LARS, and related methods.]]></description>
<dc:subject>ridge-regression shrinkage machine-learning high-dimensional lasso slides notes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:e188bb48b5c7/</dc:identifier>
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</item>
<item rdf:about="http://www.cs.ucr.edu/~eamonn/time_series_data/">
    <title>UCR Time Series Classification/Clustering Page</title>
    <dc:date>2012-12-27T22:11:45+00:00</dc:date>
    <link>http://www.cs.ucr.edu/~eamonn/time_series_data/</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[Time series data for classification and clustering.]]></description>
<dc:subject>datasets classification clustering time-series machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:74e82f7aea02/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:time-series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:machine-learning"/>
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</item>
<item rdf:about="http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage?from=Main.Textbook">
    <title>Bayesian Reasoning and Machine learning</title>
    <dc:date>2012-11-08T22:32:36+00:00</dc:date>
    <link>http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage?from=Main.Textbook</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>textbook machine-learning book bayes</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:6386aaa11e17/</dc:identifier>
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</item>
<item rdf:about="http://i.stanford.edu/~ullman/mmds.html">
    <title>Mining of Massive Datasets</title>
    <dc:date>2012-10-11T16:42:10+00:00</dc:date>
    <link>http://i.stanford.edu/~ullman/mmds.html</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>textbook data machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:0e4f8804e723/</dc:identifier>
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<item rdf:about="http://www.pnas.org/content/106/21/8519.full.pdf">
    <title>Paper on Automated Flow Cyometry Data Analysis</title>
    <dc:date>2012-08-06T04:03:37+00:00</dc:date>
    <link>http://www.pnas.org/content/106/21/8519.full.pdf</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[PNAS paper. GJ McLachlan is a co-author.]]></description>
<dc:subject>flow-cytometry machine-learning high-dimensional</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:ec4afd28494d/</dc:identifier>
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<item rdf:about="http://www.hindawi.com/journals/abi/2009/584603/">
    <title>A Survey of Flow Cytometry Data Analysis Methods</title>
    <dc:date>2012-08-06T03:45:08+00:00</dc:date>
    <link>http://www.hindawi.com/journals/abi/2009/584603/</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>flow-cytometry machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:bef37befc6f3/</dc:identifier>
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</item>
<item rdf:about="http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/">
    <title>Analysis of Microarray Data: Lectures</title>
    <dc:date>2012-08-04T05:33:20+00:00</dc:date>
    <link>http://bioinformatics.mdanderson.org/MicroarrayCourse/Lectures09/</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>multivariate microarray tutorial machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:97fcde78bf90/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:tutorial"/>
	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:machine-learning"/>
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</item>
<item rdf:about="http://freakonometrics.blog.free.fr/index.php?post/2012/02/15/MAT8886-reducing-dimension-using-factors">
    <title>reducing dimension using factors - Freakonometrics</title>
    <dc:date>2012-03-25T20:16:30+00:00</dc:date>
    <link>http://freakonometrics.blog.free.fr/index.php?post/2012/02/15/MAT8886-reducing-dimension-using-factors</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>dimension-reduction multivariate statistics machine-learning PCA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:4d00957e6aeb/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:machine-learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:PCA"/>
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</item>
<item rdf:about="http://work.caltech.edu/paper/CaltechNews.pdf">
    <title>Teacher Builds on the Basics</title>
    <dc:date>2012-03-14T17:46:58+00:00</dc:date>
    <link>http://work.caltech.edu/paper/CaltechNews.pdf</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[Article about the great teacher, Yaser Abu-Mostafa, from CalTech.]]></description>
<dc:subject>teaching motivational pattern-recognition machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:80eeb44c83c5/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:machine-learning"/>
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</item>
<item rdf:about="http://mldata.org/">
    <title>mldata.org ::: machine learning data set repository</title>
    <dc:date>2012-03-14T16:41:13+00:00</dc:date>
    <link>http://mldata.org/</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>datasets machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:6d41de8b79a0/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:machine-learning"/>
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</item>
<item rdf:about="http://blog.rtwilson.com/review-machine-learning-an-algorithmic-perspective-by-stephen-marsland/">
    <title>Review: Machine Learning: An Algorithmic Perspective by Stephen Marsland</title>
    <dc:date>2012-03-04T00:05:50+00:00</dc:date>
    <link>http://blog.rtwilson.com/review-machine-learning-an-algorithmic-perspective-by-stephen-marsland/</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>book-review machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:d0e9d481bdd0/</dc:identifier>
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</item>
<item rdf:about="http://pindancing.blogspot.com/2010/01/learning-about-machine-learniing.html">
    <title>Pin Dancing: Learning about Machine Learning</title>
    <dc:date>2012-03-03T23:54:15+00:00</dc:date>
    <link>http://pindancing.blogspot.com/2010/01/learning-about-machine-learniing.html</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>machine-learning tutorial statistics linear-algebra reinforcement-learning neural-networks computer-vision robotics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:9073e159fe0b/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:linear-algebra"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:computer-vision"/>
	<rdf:li rdf:resource="https://pinboard.in/u:ramhiser/t:robotics"/>
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</item>
<item rdf:about="http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial">
    <title>Unsupervised Feature Learning and Deep Learning Tutorial</title>
    <dc:date>2012-03-03T23:42:33+00:00</dc:date>
    <link>http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>machine-learning statistics deep-learning unsupervised-learning</dc:subject>
<dc:source>https://instapaper.com/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:66b31472d92d/</dc:identifier>
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</item>
<item rdf:about="http://norvig.com/spell-correct.html">
    <title>How to Write a Spelling Corrector</title>
    <dc:date>2012-03-02T16:54:05+00:00</dc:date>
    <link>http://norvig.com/spell-correct.html</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>machine-learning python spelling-corrector bayes statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:3b3d32cd2b2f/</dc:identifier>
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</item>
<item rdf:about="http://flowingdata.com/2012/02/03/an-action-plan-for-data-science-a-decade-ago/">
    <title>An action plan for data science, a decade ago</title>
    <dc:date>2012-03-01T16:09:21+00:00</dc:date>
    <link>http://flowingdata.com/2012/02/03/an-action-plan-for-data-science-a-decade-ago/</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[A discussion about data science and William Cleveland's initial definition and paper.]]></description>
<dc:subject>data-science statistics data visualization machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:220f8e77a6da/</dc:identifier>
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<item rdf:about="http://www.causality.inf.ethz.ch/activelearning.php?page=datasets">
    <title>Active Learning Challenge</title>
    <dc:date>2012-02-29T22:11:02+00:00</dc:date>
    <link>http://www.causality.inf.ethz.ch/activelearning.php?page=datasets</link>
    <dc:creator>ramhiser</dc:creator><description><![CDATA[The site contains several data sets that are used to benchmark active learning methods. A thorough discussion is given on how to evaluate active learning methods for the given data sets.]]></description>
<dc:subject>machine-learning active-learning datasets data-competition benchmark</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:e2653130af48/</dc:identifier>
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<item rdf:about="http://metamarkets.com/2011/machine-learning-in-wonderland/">
    <title>Why Generic Machine Learning Fails</title>
    <dc:date>2012-02-21T16:13:26+00:00</dc:date>
    <link>http://metamarkets.com/2011/machine-learning-in-wonderland/</link>
    <dc:creator>ramhiser</dc:creator><dc:subject>data prediction classification machine-learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:ramhiser/b:3975fefe24f3/</dc:identifier>
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