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    <description>recent bookmarks from wrrn</description>
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	<rdf:li rdf:resource="http://stackoverflow.com/questions/19626530/python-xticks-in-subplots"/>
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	<rdf:li rdf:resource="http://blog.yhathq.com/posts/logistic-regression-and-python.html"/>
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	<rdf:li rdf:resource="http://www.slideshare.net/srowen/matrix-factorization"/>
	<rdf:li rdf:resource="http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html"/>
	<rdf:li rdf:resource="http://www.cookbook-r.com/"/>
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	<rdf:li rdf:resource="http://blog.yhathq.com/posts/r-lm-summary.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.1548"/>
	<rdf:li rdf:resource="http://googledata.org/google-research/free-language-lessons-for-computers/"/>
	<rdf:li rdf:resource="http://work.caltech.edu/library/?cmp=tw-strata-confreg-home-stsc14_twitter_posts"/>
	<rdf:li rdf:resource="http://markus.com/deep-learning-101/"/>
	<rdf:li rdf:resource="http://phenomena.nationalgeographic.com/2013/07/19/how-forensic-linguistics-outed-j-k-rowling-not-to-mention-james-madison-barack-obama-and-the-rest-of-us/"/>
	<rdf:li rdf:resource="http://gigaom.com/2013/06/07/under-the-covers-of-the-nsas-big-data-effort/"/>
	<rdf:li rdf:resource="http://www.independent.co.uk/voices/comment/immigration-crime-benefits-everything-you-know-about-the-state-of-the-nation-is-wrong-8697574.html"/>
	<rdf:li rdf:resource="http://projects.csail.mit.edu/church/wiki/Church"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1306.0239"/>
	<rdf:li rdf:resource="http://www.solers.com/BAAinfo-reg/ppaml/"/>
	<rdf:li rdf:resource="http://techcrunch.com/2013/05/09/desire2learns-new-learning-suite-aims-to-predict-success-change-how-students-navigate-their-academic-career/"/>
	<rdf:li rdf:resource="https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers#readme"/>
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	<rdf:li rdf:resource="http://www.kenvanharen.com/2012/11/getting-started-with-ramp-detecting.html"/>
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	<rdf:li rdf:resource="http://blog.hooktheory.com/2012/06/06/i-analyzed-the-chords-of-1300-popular-songs-for-patterns-this-is-what-i-found/"/>
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	<rdf:li rdf:resource="http://www.win-vector.com/blog/2012/05/the-differing-perspectives-of-statistics-and-machine-learning/"/>
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	<rdf:li rdf:resource="http://blog.biophysengr.net/2012/03/eigenbracket-2012-using-graph-theory-to.html"/>
	<rdf:li rdf:resource="http://lesswrong.com/lw/aq9/decision_theories_a_less_wrong_primer/"/>
	<rdf:li rdf:resource="http://nlp.stanford.edu/IR-book/html/htmledition/classification-with-more-than-two-classes-1.html#sec%3amore-than-two-classes"/>
	<rdf:li rdf:resource="http://annezelenka.com/2012/01/07/how-data-science-is-like-magic/"/>
	<rdf:li rdf:resource="https://simple-note.appspot.com/publish/n46v6N"/>
	<rdf:li rdf:resource="http://www.fastcompany.com/1814225/law-enforcements-secret-weapon-google-maps?partner=rss"/>
	<rdf:li rdf:resource="http://blogs.lse.ac.uk/politicsandpolicy/2012/02/23/coalition-termination-hanretty/"/>
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  </channel><item rdf:about="http://www.computervisionblog.com/2015/04/deep-learning-vs-probabilistic.html">
    <title>Tombone's Computer Vision Blog: Deep Learning vs Probabilistic Graphical Models vs Logic</title>
    <dc:date>2026-03-27T02:41:20+00:00</dc:date>
    <link>http://www.computervisionblog.com/2015/04/deep-learning-vs-probabilistic.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Deep Learning and Machine Learning (Data-Driven Machines)
Machine Learning is about learning from examples and today's state-of-the-art recognition techniques require a lot of training data, a deep neural network, and patience. Logic has long advanced beyond what you describe ("modus ponens", wtf??) - there are probabilistic logics, statistical-relational learning, neuro-symbolic representation, stochastic logic, first-order graphical models and many other logic-based approaches which allow for statistical learning, dealing with partial observability, uncertainty and noise, etc.]]></description>
<dc:subject>ai learning machinelearning ComputerVision DeepLearning statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:816b07c3e568/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:ComputerVision"/>
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<item rdf:about="http://colah.github.io/posts/2015-09-Visual-Information/">
    <title>Visual Information Theory -- colah's blog</title>
    <dc:date>2026-03-18T01:12:08+00:00</dc:date>
    <link>http://colah.github.io/posts/2015-09-Visual-Information/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[There is no code which, for this distribution, will give us an average codeword length of less than 1.75 bits. For example, if we need to send a codeword that is 4 bits long 50% of the time, our average message length is 2 bits longer than it would be if we weren’t sending that codeword.]]></description>
<dc:subject>information language information-theory visualization statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:4b6a14c225f0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:information-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:visualization"/>
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<item rdf:about="https://github.com/sapientinc/HRM">
    <title>GitHub - sapientinc/HRM: Hierarchical Reasoning Model Official Release</title>
    <dc:date>2026-03-16T23:12:19+00:00</dc:date>
    <link>https://github.com/sapientinc/HRM</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[🧩
# Download and build Sudoku dataset
python dataset/build_sudoku_dataset.py --output-dir data/sudoku-extreme-1k-aug-1000 --subsample-size 1000 --num-aug 1000
# Start training (single GPU, smaller batch size)
OMP_NUM_THREADS=8 python pretrain.py data_path=data/sudoku-extreme-1k-aug-1000 epochs=20000 eval_interval=2000 global_batch_size=384 lr=7e-5 puzzle_emb_lr=7e-5 weight_decay=1.0 puzzle_emb_weight_decay=1.0
Runtime: ~10 hours on a RTX 4070 laptop GPU
To use the checkpoints, see Evaluation section below. ARC-1:
OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py
Runtime: ~24 hours
ARC-2:
OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/arc-2-aug-1000
Runtime: ~24 hours (checkpoint after 8 hours is often sufficient)
Sudoku Extreme (1k):
OMP_NUM_THREADS=8 torchrun --nproc-per-node 8 pretrain.py data_path=data/sudoku-extreme-1k-aug-1000 epochs=20000 eval_interval=2000 lr=1e-4 puzzle_emb_lr=1e-4 weight_decay=1.0 puzzle_emb_weight_decay=1.0
Runtime: ~10 minut]]></description>
<dc:subject>ai MachineLearning statistics research</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:0e122b0ad086/</dc:identifier>
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<item rdf:about="https://probmods.org/">
    <title>Probabilistic Models of Cognition</title>
    <dc:date>2017-02-05T20:22:52+00:00</dc:date>
    <link>https://probmods.org/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[In this book, we explore the probabilistic approach to cognitive science, which models learning and reasoning as inference in complex probabilistic models. In particular, we examine how a broad range of empirical phenomena in cognitive science (including intuitive physics, concept learning, causal reasoning, social cognition, and language understanding) can be modeled using a functional probabilistic programming language called Church.]]></description>
<dc:subject>cognition books probability statistics MachineLearning mind</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:1a22b9ccb8d1/</dc:identifier>
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<item rdf:about="https://www.gitbook.com/book/ds8/textbook/details">
    <title>Computational and Inferential Thinking · GitBook</title>
    <dc:date>2016-09-29T14:00:25+00:00</dc:date>
    <link>https://www.gitbook.com/book/ds8/textbook/details</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[For whatever aspect of the world we wish to study---whether it's the Earth's weather, the world's markets, political polls, or the human mind---data we collect typically offer an incomplete description of the subject at hand. The central challenge of data science is to make reliable conclusions using this partial information.]]></description>
<dc:subject>data data-science MachineLearning statistics mathematics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:002c635f241c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
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<item rdf:about="http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/PPRPAGES/pprtut.htm">
    <title>Tutorials on Topics in Statistical Pattern Recognition</title>
    <dc:date>2016-07-05T10:47:25+00:00</dc:date>
    <link>http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/PPRPAGES/pprtut.htm</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[ a good collection of resources.]]></description>
<dc:subject>statistics MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:a71743e301b8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
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<item rdf:about="https://medium.com/user-experience-design-1/facebook-and-how-uis-twist-your-words-4ceedc5fd93#.dhzeguvdt">
    <title>Facebook and How UIs Twist Your Words — User Experience Design (UX) — Medium</title>
    <dc:date>2016-01-14T17:16:52+00:00</dc:date>
    <link>https://medium.com/user-experience-design-1/facebook-and-how-uis-twist-your-words-4ceedc5fd93#.dhzeguvdt</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[This UI has a special role: it is a social mediator. It’s standing in for the user and speaking for them; the UI becomes part of their digital body language. This is a huge amount of power and responsibility. As this study has shown, while social platforms bring us together, UI missteps can push us apart.]]></description>
<dc:subject>facebook statistics UI identity interface language</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:520ef4b90986/</dc:identifier>
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<item rdf:about="http://gitxiv.com/posts/jS9LJ5kh9ny6iqD7Z/human-level-concept-learning-through-probabilistic-program">
    <title>Human-level concept learning through probabilistic program induction | GitXiv</title>
    <dc:date>2015-12-14T12:52:01+00:00</dc:date>
    <link>http://gitxiv.com/posts/jS9LJ5kh9ny6iqD7Z/human-level-concept-learning-through-probabilistic-program</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches]]></description>
<dc:subject>AI MachineLearning ComputerVision probabilistic-programming statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:fdf7810bf27e/</dc:identifier>
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<item rdf:about="https://source.opennews.org/en-US/articles/introducing-agate/">
    <title>Introducing agate: a Better Data Analysis Library for Journalists - Features - Source: An OpenNews project</title>
    <dc:date>2015-10-29T10:28:54+00:00</dc:date>
    <link>https://source.opennews.org/en-US/articles/introducing-agate/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[agate is a Python data analysis library in the vein of numpy or pandas, but with one crucial difference. Whereas those libraries optimize for the needs of scientists—namely, being incredibly fast when working with vast numerical datasets—agate instead optimizes for the performance of the human who is using it.]]></description>
<dc:subject>data python journalism data-mining data-science statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:0e00df3cd374/</dc:identifier>
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<item rdf:about="http://magng.com/thought-vectors-could-revolutionize-artificial-intelligence/">
    <title>‘Thought vectors’ could revolutionize artificial intelligence - Magng</title>
    <dc:date>2015-05-29T09:30:49+00:00</dc:date>
    <link>http://magng.com/thought-vectors-could-revolutionize-artificial-intelligence/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[The underlying idea is that by ascribing every word a set of numbers (or vector), a computer can be trained to understand the actual meaning of these words.]]></description>
<dc:subject>ai MachineLearning future mathematics statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:b27777611726/</dc:identifier>
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<item rdf:about="http://phys.org/news/2015-04-probabilistic-lines-code-thousands.html">
    <title>Probabilistic programming does in 50 lines of code what used to take thousands</title>
    <dc:date>2015-04-15T12:04:10+00:00</dc:date>
    <link>http://phys.org/news/2015-04-probabilistic-lines-code-thousands.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[To make machine-learning applications easier to build, computer scientists have begun developing so-called probabilistic programming languages, which let researchers mix and match machine-learning techniques that have worked well in other contexts. ]]></description>
<dc:subject>programming probability statistics probabilistic-programming MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:440fc41b012b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:probabilistic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://radar.oreilly.com/2015/02/network-structure-and-dynamics-in-online-social-systems.html">
    <title>Network structure and dynamics in online social systems - O'Reilly Radar</title>
    <dc:date>2015-02-05T16:19:51+00:00</dc:date>
    <link>http://radar.oreilly.com/2015/02/network-structure-and-dynamics-in-online-social-systems.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Thinking of a social network as an information transport layer, Kleinberg and his colleagues instead set out to track the evolution of cascades. In the process, they framed an interesting balanced algorithmic prediction problem: given a cascade of size k, predict whether it will reach size 2k (it turns out 2k is roughly the median size of a cascade conditional on whether it reaches size k).]]></description>
<dc:subject>networks prediction MachineLearning statistics beinghuman</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:07c9d162d41a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:beinghuman"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://futureoflife.org/misc/open_letter">
    <title>FLI - Future of Life Institute</title>
    <dc:date>2015-01-11T21:08:00+00:00</dc:date>
    <link>http://futureoflife.org/misc/open_letter</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. ]]></description>
<dc:subject>ai future research MachineLearning statistics humancomputer society</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:7d9d55d57835/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:future"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:humancomputer"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:society"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://pystruct.github.io/intro.html#intro">
    <title>What is structured learning? — pystruct 0.1 documentation</title>
    <dc:date>2014-10-08T20:39:04+00:00</dc:date>
    <link>http://pystruct.github.io/intro.html#intro</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Structured prediction is a generalization of the standard paradigms of supervised learning, classification and regression. All of these can be thought of finding a function that minimizes some loss over a training set. The differences are in the kind of functions that are used and the losses.]]></description>
<dc:subject>MachineLearning StructuredLearning statistics research tools python</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:644863a500c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:StructuredLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blogs.ft.com/money-supply/2014/05/29/sex-drugs-and-gdp-calculating-illicit-trade/">
    <title>Sex, drugs and GDP – what’s going on? | Money Supply</title>
    <dc:date>2014-10-01T17:09:00+00:00</dc:date>
    <link>http://blogs.ft.com/money-supply/2014/05/29/sex-drugs-and-gdp-calculating-illicit-trade/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Heroin imports. Crack cocaine sales. Home-grown cannabis. And the turnover of the UK’s brothels: all are now part of the UK’s GDP data, and have given it a welcome boost.]]></description>
<dc:subject>sex drugs uk eu economics politics statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:1c14a1ed3541/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:sex"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:drugs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:uk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:eu"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://vudlab.com/simpsons/">
    <title>Simpson's Paradox</title>
    <dc:date>2014-09-17T11:59:00+00:00</dc:date>
    <link>http://vudlab.com/simpsons/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[In 1973, the University of California-Berkeley was sued for sex discrimination. The numbers looked pretty incriminating: the graduate schools had just accepted 44% of male applicants but only 35% of female applicants. When researchers looked at the evidence, though, they uncovered something surprising:]]></description>
<dc:subject>mathematics statistics visualization gender culture society</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:fe7f22b87075/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:gender"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:culture"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:society"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nbviewer.ipython.org/github/jakevdp/sklearn_scipy2013/blob/master/rendered_notebooks/02.1_representation_of_data.ipynb">
    <title>Representation and Visualization of Data</title>
    <dc:date>2014-07-08T18:28:47+00:00</dc:date>
    <link>http://nbviewer.ipython.org/github/jakevdp/sklearn_scipy2013/blob/master/rendered_notebooks/02.1_representation_of_data.ipynb</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[By the end of this section you should:

Know the internal data representation of scikit-learn.
Know how to use scikit-learn's dataset loaders to load example data.
Know how to turn image & text data into data matrices for learning.
Know how to use matplotlib to help visualize different types of data.]]></description>
<dc:subject>statistics MachineLearning data-science sklearn python learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:22bd761aa4f1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:sklearn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nbviewer.ipython.org/github/justmarkham/gadsdc1/blob/master/logistic_assignment/kevin_logistic_sklearn.ipynb">
    <title>Logistic Regression with scikit-learn</title>
    <dc:date>2014-07-08T17:02:27+00:00</dc:date>
    <link>http://nbviewer.ipython.org/github/justmarkham/gadsdc1/blob/master/logistic_assignment/kevin_logistic_sklearn.ipynb</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[This is an example of logistic regression in Python with the scikit-learn module, performed for an assignment with my General Assembly Data Science class. The dataset I chose is the affairs dataset that comes with Statsmodels. It was derived from a survey of women in 1974 by Redbook magazine, in which married women were asked about their participation in extramarital affairs. More information about the study is available in a 1978 paper from the Journal of Political Economy.]]></description>
<dc:subject>data-science python pandas sklean statistics learning MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:820ba6967da2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:pandas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:sklean"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/jakevdp/sklearn_pycon2013">
    <title>PyCon 2013 Scikit-learn Tutorial</title>
    <dc:date>2014-07-08T16:39:47+00:00</dc:date>
    <link>https://github.com/jakevdp/sklearn_pycon2013</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[To get a grip on how to do machine learning with scikit-learn, it is worth working through the entire set of notebooks at: https://github.com/jakevdp/sklearn_pycon2013 . These go relatively fast, are fun to read]]></description>
<dc:subject>python data-science sklearn statistics data learning MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:e9af7fc15aa6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:sklearn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/regression_diagnostics.html">
    <title>Regression diagnostics — statsmodels 0.6.0 documentation</title>
    <dc:date>2014-07-08T15:55:32+00:00</dc:date>
    <link>http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/regression_diagnostics.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[This example file shows how to use a few of the statsmodels regression diagnostic tests in a real-life context. You can learn about more tests and find out more information abou the tests here on the Regression Diagnostics page.]]></description>
<dc:subject>statistics regression data python tools data-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:f88aeff825f0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://statsmodels.sourceforge.net/stable/diagnostic.html">
    <title>Regression Diagnostics and Specification Tests — statsmodels 0.5.0 documentation</title>
    <dc:date>2014-07-08T15:54:52+00:00</dc:date>
    <link>http://statsmodels.sourceforge.net/stable/diagnostic.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[One solution to the problem of uncertainty about the correct specification is to use robust methods, for example robust regression or robust covariance (sandwich) estimators. The second approach is to test whether our sample is consistent with these assumptions.

The following briefly summarizes specification and diagnostics tests for linear regression.]]></description>
<dc:subject>regression statistics data-mining tools python data-science MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:8b3b882b0917/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://statsmodels.sourceforge.net/devel/examples/">
    <title>Statsmodels Examples — statsmodels 0.6.0 documentation</title>
    <dc:date>2014-07-08T15:48:12+00:00</dc:date>
    <link>http://statsmodels.sourceforge.net/devel/examples/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[This page provides a series of examples, tutorials and recipes to help you get started with statsmodels. Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository.
]]></description>
<dc:subject>python statistics data data-mining tools data-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:612635b82593/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://bokeh.pydata.org/">
    <title>Welcome to Bokeh — Bokeh 0.4.4 documentation</title>
    <dc:date>2014-07-08T09:52:55+00:00</dc:date>
    <link>http://bokeh.pydata.org/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, but also deliver this capability with high-performance interactivity over very large or streaming datasets.]]></description>
<dc:subject>data javascript python visualization data-mining statistics data-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:c3fe42a4213b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:javascript"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://statsmodels.sourceforge.net/stable/discretemod.html">
    <title>Regression with Discrete Dependent Variable — statsmodels 0.5.0 documentation</title>
    <dc:date>2014-07-08T09:44:44+00:00</dc:date>
    <link>http://statsmodels.sourceforge.net/stable/discretemod.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Regression models for limited and qualitative dependent variables. The module currently allows the estimation of models with binary (Logit, Probit), nominal (MNLogit), or count (Poisson) data.]]></description>
<dc:subject>statistics regression data tools python sklearn</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:6c192b5b8c33/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:sklearn"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://statsmodels.sourceforge.net/devel/example_formulas.html">
    <title>Fitting models using R-style formulas — statsmodels 0.6.0 documentation</title>
    <dc:date>2014-07-08T09:38:51+00:00</dc:date>
    <link>http://statsmodels.sourceforge.net/devel/example_formulas.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas and data to the matrices that are used in model fitting. The formula framework is quite powerful; this tutorial only scratches the surface. ]]></description>
<dc:subject>python pandas data data-mining statistics tools data-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:6a15df54e73c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:pandas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://stackoverflow.com/questions/19626530/python-xticks-in-subplots">
    <title>matplotlib - Python xticks in subplots - Stack Overflow</title>
    <dc:date>2014-07-04T15:48:21+00:00</dc:date>
    <link>http://stackoverflow.com/questions/19626530/python-xticks-in-subplots</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[How can I change my xticks just for one of these subplots? I can only access the axes of the subplots with axarr[i, j]. How can I access "plt" just for one particular subplot?]]></description>
<dc:subject>matplotlib python statistics visualization information</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:166a9e3d2dca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:matplotlib"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:information"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://scipy-lectures.github.io/intro/matplotlib/matplotlib.html">
    <title>1.4. Matplotlib: plotting — Scipy lecture notes</title>
    <dc:date>2014-07-04T13:46:20+00:00</dc:date>
    <link>https://scipy-lectures.github.io/intro/matplotlib/matplotlib.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Matplotlib is probably the single most used Python package for 2D-graphics. It provides both a very quick way to visualize data from Python and publication-quality figures in many formats. We are going to explore matplotlib in interactive mode covering most common cases.]]></description>
<dc:subject>matplotlib python data visualization statistics learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:e062260f2aab/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:matplotlib"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.yhathq.com/posts/logistic-regression-and-python.html">
    <title>ŷhat | Logistic Regression in Python</title>
    <dc:date>2014-07-04T12:42:26+00:00</dc:date>
    <link>http://blog.yhathq.com/posts/logistic-regression-and-python.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Python is one of the most popular languages for machine learning, and while there are bountiful resources covering topics like Support Vector Machines and text classification using Python, there's far less material on logistic regression.

This is a post about using logistic regression in Python.]]></description>
<dc:subject>python statistics regression pandas data-science learning MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:0c39c8f2ebca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:pandas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nbviewer.ipython.org/github/cs109/content/blob/master/labs/lab4/Lab4full.ipynb">
    <title>Scikit-Learn, Regression, and PCA, and still more regression.</title>
    <dc:date>2014-07-04T12:39:34+00:00</dc:date>
    <link>http://nbviewer.ipython.org/github/cs109/content/blob/master/labs/lab4/Lab4full.ipynb</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[First, a bit about scikit-learn. Some of the following text is taken from the scikit-learn API paper: http://arxiv.org/pdf/1309.0238v1.pdf

All objects within scikit-learn share a uniform common basic API consisting of three complementary interfaces: an estimator interface for building and ﬁtting models, a predictor interface for making predictions and a transformer interface for converting data.]]></description>
<dc:subject>statistics regression matplotlib python sklearn data-science learning MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:73eeee149b11/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:matplotlib"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:sklearn"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nbviewer.ipython.org/github/cs109/content/blob/master/labs/lab3/lab3full.ipynb">
    <title>Exploratory Data Analysis for Classification using Pandas and Matplotlib</title>
    <dc:date>2014-07-04T12:38:09+00:00</dc:date>
    <link>http://nbviewer.ipython.org/github/cs109/content/blob/master/labs/lab3/lab3full.ipynb</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Preliminary plotting stuff to get things going]]></description>
<dc:subject>data pandas matplotlib python statistics data-science learning MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:d29093646e07/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:pandas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:matplotlib"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nbviewer.ipython.org/gist/olgabot/5357268">
    <title>Implementation of typographic and design principles in matplotlib and iPython notebook</title>
    <dc:date>2014-07-04T12:32:58+00:00</dc:date>
    <link>http://nbviewer.ipython.org/gist/olgabot/5357268</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Bad design = difficult interpretation, possible loss of information, and inability to recognize trends.]]></description>
<dc:subject>design information visualization python matplotlib statistics learning MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:d8b458281c8a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:design"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:matplotlib"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.slideshare.net/srowen/matrix-factorization">
    <title>Simple Matrix Factorization for Recommendation</title>
    <dc:date>2014-05-20T19:00:49+00:00</dc:date>
    <link>http://www.slideshare.net/srowen/matrix-factorization</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[A quick introduction to the intuition behind matrix factorization as applied to recommenders, and the alternating-least-squares approach in particular]]></description>
<dc:subject>MachineLearning mathematics statistics recommendations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:7317e2756d13/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:recommendations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html">
    <title>homepages.inf.ed.ac.uk/vlavrenk/iaml.html</title>
    <dc:date>2014-05-20T11:03:15+00:00</dc:date>
    <link>http://homepages.inf.ed.ac.uk/vlavrenk/iaml.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[slides and audio from university course. Watch along on YouTube.]]></description>
<dc:subject>learning video MachineLearning algorithms computing statistics theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:391aae1ac563/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cookbook-r.com/">
    <title>Cookbook for R » Cookbook for R</title>
    <dc:date>2014-05-14T16:17:17+00:00</dc:date>
    <link>http://www.cookbook-r.com/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Welcome to the Cookbook for R (formerly named R Cookbook). The goal of the cookbook is to provide solutions to common tasks and problems in analyzing data.

Most of the code in these pages can be copied and pasted into the R command window if you want to see them in action]]></description>
<dc:subject>code data programming statistics language R MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:3b9feda1a795/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:code"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://personality-project.org/r/r.lm.html">
    <title>R Guide -- the linear model</title>
    <dc:date>2014-04-15T15:15:59+00:00</dc:date>
    <link>https://personality-project.org/r/r.lm.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Many statistics used by psychologists and social scientists are special cases of the linear model. Generalizations of the linear model include an even wider range of statistical models.

Consider the following models]]></description>
<dc:subject>statistics data-science data models psychology MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:9eae6280855e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.coursera.org/specialization/jhudatascience/1">
    <title>Specialization | Coursera</title>
    <dc:date>2014-04-15T15:07:51+00:00</dc:date>
    <link>https://www.coursera.org/specialization/jhudatascience/1</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[This specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results]]></description>
<dc:subject>data-science coursera statistics learning data MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:cf749f536bdd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:coursera"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://data.princeton.edu/R/linearModels.html">
    <title>R - Linear Models</title>
    <dc:date>2014-04-15T15:01:33+00:00</dc:date>
    <link>http://data.princeton.edu/R/linearModels.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Let us try some linear models, starting with multiple regression and analysis of covariance models, and then moving on to models using regression splines.]]></description>
<dc:subject>models regression statistics learning MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:fd08304048c2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.tc3.edu/instruct/sbrown/swt/symbol.htm">
    <title>Symbol Sheet / SWT / Brown, TC3</title>
    <dc:date>2014-04-15T15:01:07+00:00</dc:date>
    <link>http://www.tc3.edu/instruct/sbrown/swt/symbol.htm</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Here are symbols for various sample statistics and the corresponding population parameters. They are not repeated in the list below.]]></description>
<dc:subject>statistics language notation MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:3b63714b591f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:notation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.yhathq.com/posts/r-lm-summary.html">
    <title>ŷhat | Fitting &amp; Interpreting Linear Models in R</title>
    <dc:date>2014-04-15T15:00:06+00:00</dc:date>
    <link>http://blog.yhathq.com/posts/r-lm-summary.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[This is a post about linear models in R, how to interpret lm results, and common rules of thumb to help side-step the most common mistakes.]]></description>
<dc:subject>data statistics regression R models MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:e50e52d69890/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.1548">
    <title>[1312.1548] Model trees with topic model preprocessing: An approach for data journalism illustrated with the WikiLeaks Afghanistan war logs</title>
    <dc:date>2014-04-11T16:01:46+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.1548</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[using the WikiLeaks Afghanistan war logs for illustration, we present an approach that builds intelligible statistical models for interpretable segments in the data, in this case to explore the fatality rates associated with different circumstances in the Afghanistan war. ]]></description>
<dc:subject>semantic language information-retrieval wikileaks beinghuman statistics research MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:867d002b9b40/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:semantic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:information-retrieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:wikileaks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:beinghuman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://googledata.org/google-research/free-language-lessons-for-computers/">
    <title>Free Language Lessons for Computers | Google Data</title>
    <dc:date>2013-12-04T23:01:53+00:00</dc:date>
    <link>http://googledata.org/google-research/free-language-lessons-for-computers/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Here’s a listing of the major datasets we’ve released in the last year, or you can subscribe to our mailing list. Please tell us what you’ve managed to accomplish, or send us pointers to papers that use this data. We want to see what the research world can do with what we’ve created.]]></description>
<dc:subject>MachineLearning tools data google data-mining statistics corpus connectionmachine data-science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:1c09da723a3a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:corpus"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:connectionmachine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://work.caltech.edu/library/?cmp=tw-strata-confreg-home-stsc14_twitter_posts">
    <title>Machine Learning Video Library - Learning From Data (Abu-Mostafa)</title>
    <dc:date>2013-12-04T14:04:18+00:00</dc:date>
    <link>http://work.caltech.edu/library/?cmp=tw-strata-confreg-home-stsc14_twitter_posts</link>
    <dc:creator>wrrn</dc:creator><dc:subject>MachineLearning probability statistics Ai bayesian video learning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:f9eb4595f1ca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:Ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:video"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://markus.com/deep-learning-101/">
    <title>Deep Learning 101</title>
    <dc:date>2013-11-15T09:50:35+00:00</dc:date>
    <link>http://markus.com/deep-learning-101/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[My goal is to give you a layman understanding of what deep learning actually is so you can follow some of my thesis research this year as well as mentally filter out news articles that sensationalize these buzzwords.]]></description>
<dc:subject>ai MachineLearning DeepLearning statistics algorithms learning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:c0a359f195a5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:DeepLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://phenomena.nationalgeographic.com/2013/07/19/how-forensic-linguistics-outed-j-k-rowling-not-to-mention-james-madison-barack-obama-and-the-rest-of-us/">
    <title>How Forensic Linguistics Outed J.K. Rowling (Not to Mention James Madison, Barack Obama, and the Rest of Us) – Phenomena: Only Human</title>
    <dc:date>2013-07-22T12:54:12+00:00</dc:date>
    <link>http://phenomena.nationalgeographic.com/2013/07/19/how-forensic-linguistics-outed-j-k-rowling-not-to-mention-james-madison-barack-obama-and-the-rest-of-us/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[I wanted to know how those computer scientists did their mysterious linguistic analyses. I called both of them yesterday and learned not only how the Rowling investigation worked, but about the fascinating world of forensic linguistics.]]></description>
<dc:subject>forensics statistics linguistics writing semantic MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:dab87f222bcd/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:forensics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:writing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:semantic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://gigaom.com/2013/06/07/under-the-covers-of-the-nsas-big-data-effort/">
    <title>Under the covers of the NSA’s big data effort — Tech News and Analysis</title>
    <dc:date>2013-07-18T16:14:50+00:00</dc:date>
    <link>http://gigaom.com/2013/06/07/under-the-covers-of-the-nsas-big-data-effort/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Baker’s hypothetical might appear pretty cut and dry but, data scientist Joseph Turian explains, call records in general probably don’t offer too strong of a signal and could lead to situations where innocent behavior patterns looks a lot like nefarious ones. “But once you start connecting the dots with other pieces of information you have from other sources,” he said via email, “you can start making more predictions.”]]></description>
<dc:subject>data-mining NSA graph database statistics probability surveillance panopticon data-science MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:97904509fced/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:NSA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:graph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:database"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:surveillance"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:panopticon"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.independent.co.uk/voices/comment/immigration-crime-benefits-everything-you-know-about-the-state-of-the-nation-is-wrong-8697574.html">
    <title>Immigration, crime, benefits: Everything you know about the state of the nation is wrong - Comment - Voices - The Independent</title>
    <dc:date>2013-07-09T13:30:25+00:00</dc:date>
    <link>http://www.independent.co.uk/voices/comment/immigration-crime-benefits-everything-you-know-about-the-state-of-the-nation-is-wrong-8697574.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Consequently, our impressions of society are formed by looking at individual factoids and scare stories as if through a long thin tube, only ever seeing a snapshot rather than the full panorama. We then depend upon cognitive biases and heuristics to fill in the gaping blank spaces.]]></description>
<dc:subject>politics uk statistics information society activisim govern MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:32348b651b27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:uk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:society"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:activisim"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:govern"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projects.csail.mit.edu/church/wiki/Church">
    <title>Church Wiki</title>
    <dc:date>2013-07-02T13:20:56+00:00</dc:date>
    <link>http://projects.csail.mit.edu/church/wiki/Church</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Church is a probabilistic programming language designed for expressive description of generative models (Goodman, Mansinghka, Roy, Bonawitz and Tenenbaum, 2008). Church is a derivative of the programming language Scheme with probabilistic semantics.]]></description>
<dc:subject>programming ai church probability bayesian statistics probabilistic-programming MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:3dc348b7b241/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:ai"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:church"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:probabilistic-programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1306.0239">
    <title>[1306.0239] Deep Learning using Support Vector Machines</title>
    <dc:date>2013-06-04T11:23:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1306.0239</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[In almost all of the previous works, hidden representation of deep networks are first learned using supervised or unsupervised techniques, and then are fed into SVMs as inputs. In contrast to those models, we are proposing to train all layers of the deep networks by backpropagating gradients through the top level SVM, learning features of all layers. Our experiments show that simply replacing softmax with linear SVMs gives significant gains ]]></description>
<dc:subject>MachineLearning DeepLearning statistics ai</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:6b8ec31860ec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:DeepLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:ai"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.solers.com/BAAinfo-reg/ppaml/">
    <title>DARPA PPAML Proposers' Day</title>
    <dc:date>2013-05-28T13:46:44+00:00</dc:date>
    <link>http://www.solers.com/BAAinfo-reg/ppaml/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[The goal of the PPAML program is to advance machine learning by using probabilistic programming to 1) dramatically increase the number of people who can successfully build machine learning applications, 2) make machine learning experts radically more effective, and 3) enable new applications that are impossible to conceive of using today’s technology. ]]></description>
<dc:subject>probability programming statistics MachineLearning future</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:98b7fcde1a27/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:future"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://techcrunch.com/2013/05/09/desire2learns-new-learning-suite-aims-to-predict-success-change-how-students-navigate-their-academic-career/">
    <title>Desire2Learn’s New Learning Suite Aims To Predict Success, Change How Students Navigate Their Academic Career | TechCrunch</title>
    <dc:date>2013-05-10T09:57:03+00:00</dc:date>
    <link>http://techcrunch.com/2013/05/09/desire2learns-new-learning-suite-aims-to-predict-success-change-how-students-navigate-their-academic-career/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[On the teacher side, Desire2Learn’s new analytics engine allows them to view predictive data visualizations that compare student performance against their peers so that they can identify at-risk students, for example, and monitor a student’s progress over time.]]></description>
<dc:subject>teaching tools information statistics prediction MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:d3b2b7afc907/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:teaching"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:tools"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers#readme">
    <title>CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers · GitHub</title>
    <dc:date>2013-03-27T12:51:05+00:00</dc:date>
    <link>https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers#readme</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Probabilistic Programming and Bayesian Methods for Hackers is designed as a introduction to Bayesian inference from a computational/understanding-first, and mathematics-second, point of view. Of course as an introductory book, we can only leave it at that: an introductory book.]]></description>
<dc:subject>python statistics bayesian Probability books MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:4b07c7c3a2e0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:Probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.greenteapress.com/thinkbayes/">
    <title>Think Bayes</title>
    <dc:date>2013-02-27T12:32:34+00:00</dc:date>
    <link>http://www.greenteapress.com/thinkbayes/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Think Bayes is an introduction to Bayesian statistics using computational methods. This version of the book is a rough draft. I am making this draft available for comments, but it comes with the warning that it is probably full of errors.]]></description>
<dc:subject>statistics books mathematics bayesian learning MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:b97a22e0d96d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://glowingpython.blogspot.co.uk/2012/10/visualizing-correlation-matrices.html">
    <title>The Glowing Python: Visualizing correlation matrices</title>
    <dc:date>2013-02-27T12:27:08+00:00</dc:date>
    <link>http://glowingpython.blogspot.co.uk/2012/10/visualizing-correlation-matrices.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[The correlation is one of the most common and most useful statistics. A correlation is a single number that describes the degree of relationship between two variables.]]></description>
<dc:subject>python statistics programming learning MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:ebffd158d71d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.eecs.berkeley.edu/~lmeyerov/projects/socioplt/viz/index.html">
    <title>Socio-PLT: Quantifying Programming Language Perceptions</title>
    <dc:date>2013-02-27T12:06:30+00:00</dc:date>
    <link>http://www.eecs.berkeley.edu/~lmeyerov/projects/socioplt/viz/index.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Why does one language succeed and another one fail? To answer questions like this, we are examining sociological aspects of programming language theory: socio-PLT. This varies from establishing first principles (see our survey of sociological research) to building socially-optimized languages. The interactive visualizations here show some of our recent efforts for a quantitative analysis of programming language perceptions.]]></description>
<dc:subject>Programming Visualization language research Statistics sociology digitalhumanities MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:a662c2ddc5c8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:Programming"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:Visualization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:research"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:Statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:sociology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:digitalhumanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it-merck-1st-place-interview/">
    <title>Deep Learning How I Did It: Merck 1st place interview</title>
    <dc:date>2013-01-13T18:51:29+00:00</dc:date>
    <link>http://blog.kaggle.com/2012/11/01/deep-learning-how-i-did-it-merck-1st-place-interview/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[What was your background prior to entering this challenge? We are a team of computer science and statistics academics. Ruslan Salakhutdinov and Geoff Hinton are professors at the University of&hellip;]]></description>
<dc:subject>.classified code models engineering speech learning networks MachineLearning statistics AI DeepLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:8852519ebc21/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:.classified"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:code"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:engineering"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:speech"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:AI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:DeepLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.kenvanharen.com/2012/11/getting-started-with-ramp-detecting.html">
    <title>kvh: Getting started with Ramp: Detecting insults</title>
    <dc:date>2012-11-29T14:04:46+00:00</dc:date>
    <link>http://www.kenvanharen.com/2012/11/getting-started-with-ramp-detecting.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Ramp is a python library for rapid machine learning prototyping. It provides a simple, declarative syntax for exploring features, algorithms and transformations quickly and efficiently.]]></description>
<dc:subject>python machinelearning nlp language linguistics statistics</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:083db7e55651/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:python"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:machinelearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:nlp"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:language"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:linguistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://deeplearning.net/">
    <title>Deep Learning</title>
    <dc:date>2012-11-29T14:00:04+00:00</dc:date>
    <link>http://deeplearning.net/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

This website is intended to host a variety of resources and pointers to information about Deep Learning.]]></description>
<dc:subject>AI MachineLearning DeepLearning computing statistics beinghuman</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:b9b00106362f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:AI"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:DeepLearning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:beinghuman"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lab.softwarestudies.com/2012/11/the-meaning-of-statistics-and-digital.html">
    <title>the meaning of statistics and digital humanities</title>
    <dc:date>2012-11-28T15:18:28+00:00</dc:date>
    <link>http://lab.softwarestudies.com/2012/11/the-meaning-of-statistics-and-digital.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[As the number of people using quantitative methods to study "cultural data" is gradually increasing (right now these people work in a few areas which do not interact: digital humanities, empirical&hellip;]]></description>
<dc:subject>statistics .classified text data graph digital MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:4956cb2faf8e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:.classified"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:text"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:graph"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:digital"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://chrishanretty.co.uk/blog/index.php/2012/11/21/replacement-risk-in-r-with-parlgov/">
    <title>Replacement risk in R with ParlGov | Chris Hanretty</title>
    <dc:date>2012-11-21T13:06:25+00:00</dc:date>
    <link>http://chrishanretty.co.uk/blog/index.php/2012/11/21/replacement-risk-in-r-with-parlgov/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[One useful concept pioneered by Robert Franzese is the idea of government replacement risk. Broadly, replacement risk is the likelihood of the incumbent government being replaced by another government which is ideologically distant]]></description>
<dc:subject>politics data statistics DigitalHumanities MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:a408da07f0e3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:DigitalHumanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.hooktheory.com/2012/06/06/i-analyzed-the-chords-of-1300-popular-songs-for-patterns-this-is-what-i-found/">
    <title>I analyzed the chords of 1300 popular songs for patterns. This is what I found. | Blog – Hooktheory</title>
    <dc:date>2012-06-14T10:09:56+00:00</dc:date>
    <link>http://blog.hooktheory.com/2012/06/06/i-analyzed-the-chords-of-1300-popular-songs-for-patterns-this-is-what-i-found/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[In this article, we’ll look at the statistics gathered from 1300 choruses, verses, etc. of popular songs to discover the answer to a few basic questions. First we’ll look at the relative popularity of different chords based on the frequency that they appear in the chord progressions of popular music. Then we’ll begin to look at the relationship that different chords have with one another.]]></description>
<dc:subject>culture data music theory statistics his MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:fd19ee1503ea/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:music"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:his"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://news.slashdot.org/story/12/05/30/0528254/statisticians-investigate-political-bias-on-wikipedia">
    <title>Statisticians Investigate Political Bias On Wikipedia - Slashdot</title>
    <dc:date>2012-05-30T13:29:01+00:00</dc:date>
    <link>http://news.slashdot.org/story/12/05/30/0528254/statisticians-investigate-political-bias-on-wikipedia</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[The team first identified 1,000 political phrases based on the number of times these phrases appeared in the text of the 2005 Congressional Record and applied statistical methods to identify the phrases that separated Democratic representatives from Republican representatives, under the model that each group speaks to its respective constituents with a distinct set of coded language. ]]></description>
<dc:subject>statistics data-mining information-retrieval connectionmachine DigitalHumanities data-science MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:d4aaf3a41772/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:information-retrieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:connectionmachine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:DigitalHumanities"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.win-vector.com/blog/2012/05/the-differing-perspectives-of-statistics-and-machine-learning/">
    <title>Win-Vector Blog</title>
    <dc:date>2012-05-08T21:04:24+00:00</dc:date>
    <link>http://www.win-vector.com/blog/2012/05/the-differing-perspectives-of-statistics-and-machine-learning/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[In both working with and thinking about machine learning and statistics I am always amazed at the differences in perspective and view between these two fields. In caricature it boils down to: machine&hellip;]]></description>
<dc:subject>.classified machine records statistics data learning MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:5854766b5658/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:.classified"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:machine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:records"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://andrewgelman.com/2012/04/how-to-mislead-with-how-to-lie-with-statistics/">
    <title>Statistical Modeling, Causal Inference, and Social Science</title>
    <dc:date>2012-04-28T14:42:03+00:00</dc:date>
    <link>http://andrewgelman.com/2012/04/how-to-mislead-with-how-to-lie-with-statistics/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Remember How to Lie With Statistics? It turns out that the author worked for the cigarette companies. John Mashey points to this, from Robert Proctor&#x2019;s book, &#x201C;Golden Holocaust: Origins&hellip;]]></description>
<dc:subject>.classified industry statistics books MachineLearning</dc:subject>
<dc:identifier>https://pinboard.in/u:wrrn/b:37d6b214afec/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:.classified"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:industry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:books"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blog.biophysengr.net/2012/03/eigenbracket-2012-using-graph-theory-to.html">
    <title>BioPhysEngr Blog: EigenBracket 2012: Using Graph Theory to Predict NCAA March Madness Basketball</title>
    <dc:date>2012-03-13T16:50:49+00:00</dc:date>
    <link>http://blog.biophysengr.net/2012/03/eigenbracket-2012-using-graph-theory-to.html</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[A simplified (and mostly accurate) way to think about this is that every team starts out with an equal number of "quality points".  Every time the computer says "Go", teams distribute their quality points to all the teams that beat them.  Thus, good teams get more quality points than they gave away (and vice versa for bad teams).  After a few rounds of this procedure, the quality points for every team approaches convergence.]]></description>
<dc:subject>graph-theory mathematics statistics prediction MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:1747cbf94453/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:graph-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://lesswrong.com/lw/aq9/decision_theories_a_less_wrong_primer/">
    <title>Decision Theories: A Less Wrong Primer - Less Wrong</title>
    <dc:date>2012-03-12T12:51:48+00:00</dc:date>
    <link>http://lesswrong.com/lw/aq9/decision_theories_a_less_wrong_primer/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[a decision theory is an algorithm for making decisions.0 The inputs are an agent's knowledge of the world, and the agent's goals and values; the output is a particular action (or plan of actions). Actually, in many cases the goals and values are implicit in the algorithm rather than given as input, but it's worth keeping them distinct in theory.]]></description>
<dc:subject>decision-trees critical_thinking game-theory statistics MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:0427a8b93e2e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:decision-trees"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:critical_thinking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:game-theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://nlp.stanford.edu/IR-book/html/htmledition/classification-with-more-than-two-classes-1.html#sec%3amore-than-two-classes">
    <title>Classification with more than two classes</title>
    <dc:date>2012-03-11T19:21:57+00:00</dc:date>
    <link>http://nlp.stanford.edu/IR-book/html/htmledition/classification-with-more-than-two-classes-1.html#sec%3amore-than-two-classes</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Classification for classes that are not mutually exclusive is called any-of , multilabel , or multivalue classification . In this case, a document can belong to several classes simultaneously, or to a single class, or to none of the classes.]]></description>
<dc:subject>statistics bayesian machine learning MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:2f39370d4e64/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:bayesian"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:machine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://annezelenka.com/2012/01/07/how-data-science-is-like-magic/">
    <title>How data science is like magic | Anne Z.</title>
    <dc:date>2012-03-06T15:52:29+00:00</dc:date>
    <link>http://annezelenka.com/2012/01/07/how-data-science-is-like-magic/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[As much as it was like anything, magic was like a language. And like a language, textbooks and teachers treated it as an orderly system for the purposes of teaching it, but in reality it was complex and chaotic and organic. It obeyed rules only to the extent that it felt like it, and there were almost as many special cases and one-time variations as there were rules. ]]></description>
<dc:subject>data-mining statistics algorithms science learning data-science MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:6a20f4864f8c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data-science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://simple-note.appspot.com/publish/n46v6N">
    <title>statistics / algorithms</title>
    <dc:date>2012-02-26T14:24:57+00:00</dc:date>
    <link>https://simple-note.appspot.com/publish/n46v6N</link>
    <dc:creator>wrrn</dc:creator><dc:subject>statistics algorithms computing data beinghuman simplenote MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:454a6c30ffe1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:algorithms"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:computing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:beinghuman"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:simplenote"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.fastcompany.com/1814225/law-enforcements-secret-weapon-google-maps?partner=rss">
    <title>Google Maps Help Predict Meth Labs Before They Open | Fast Company</title>
    <dc:date>2012-02-23T17:54:47+00:00</dc:date>
    <link>http://www.fastcompany.com/1814225/law-enforcements-secret-weapon-google-maps?partner=rss</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[Burnum and Lu examined data collected from 2002 to 2005 on seized meth lab equipment and where rogue chemists dumped the toxic by-products of methamphetamine manufacture. Map data analyzed over time successfully demonstrated the spread of meth labs throughout a metropolitan area--and even predicted where they would pop up next.]]></description>
<dc:subject>google maps opendata crime statistics probability MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:98810234802e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:google"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:maps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:opendata"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:crime"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://blogs.lse.ac.uk/politicsandpolicy/2012/02/23/coalition-termination-hanretty/">
    <title>The Coalition Government has only a 1 in 3 chance of lasting its term. Statistical modelling predicts its fall in October of 2014 | British Politics and Policy at LSE</title>
    <dc:date>2012-02-23T13:44:13+00:00</dc:date>
    <link>http://blogs.lse.ac.uk/politicsandpolicy/2012/02/23/coalition-termination-hanretty/</link>
    <dc:creator>wrrn</dc:creator><description><![CDATA[this particular model is an example of duration analysis. Duration analysis is used in lots of fields, but with different names. Engineers might talk about time-to-failure models. Epidemiologists might talk about survival models. In all cases, we’re trying to make predictions about the time until a particular event – failure of a key mechanical part, or death due to disease]]></description>
<dc:subject>politics statistics mathematics models connectionmachine MachineLearning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:wrrn/b:93b30bfbe173/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:politics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:mathematics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:connectionmachine"/>
	<rdf:li rdf:resource="https://pinboard.in/u:wrrn/t:MachineLearning"/>
</rdf:Bag></taxo:topics>
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