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<rdf:RDF xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"><channel rdf:about="https://www.bibsonomy.org/user/muehlburger/machinelearning"><title>BibSonomy bookmarks for /user/muehlburger/machinelearning</title><link>https://www.bibsonomy.org/user/muehlburger/machinelearning</link><description>BibSonomy RSS Feed for /user/muehlburger/machinelearning</description><items><rdf:Seq><rdf:li rdf:resource="http://www.cs.ubc.ca/~murphyk/MLbook/"/><rdf:li rdf:resource="http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html"/><rdf:li rdf:resource="http://www.faqs.org/faqs/ai-faq/neural-nets/part2/section-16.html"/><rdf:li rdf:resource="http://pyevolve.sourceforge.net/wordpress/?p=1747"/><rdf:li rdf:resource="http://www.datawrangling.com/some-datasets-available-on-the-web"/><rdf:li rdf:resource="http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1"/><rdf:li rdf:resource="http://www.stanford.edu/class/cs229/materials.html"/><rdf:li rdf:resource="http://videolectures.net/Top/Computer_Science/Machine_Learning/"/><rdf:li rdf:resource="http://videolectures.net/Top/Computer_Science/Network_Analysis/"/><rdf:li rdf:resource="http://nlp.stanford.edu/IR-book/html/htmledition/irbook.html"/><rdf:li rdf:resource="http://jakehofman.com/"/><rdf:li rdf:resource="http://www.cs.bilkent.edu.tr/~saksoy/courses/cs551/"/><rdf:li rdf:resource="http://zyxo.wordpress.com/"/><rdf:li rdf:resource="http://www.quora.com/What-are-the-best-blogs-about-data/answer/Peter-Skomoroch"/><rdf:li rdf:resource="http://metaoptimize.com/qa/questions/186/good-freely-available-textbooks-on-machine-learning"/><rdf:li rdf:resource="http://aima.cs.berkeley.edu/ai.html"/><rdf:li rdf:resource="http://scikit-learn.sourceforge.net/"/><rdf:li rdf:resource="http://oreilly.com/catalog/0636920017516/"/><rdf:li rdf:resource="http://ml-class.org/"/><rdf:li rdf:resource="http://openclassroom.stanford.edu/"/></rdf:Seq></items></channel><item rdf:about="http://www.cs.ubc.ca/~murphyk/MLbook/"><title>Machine Learning: a Probabilistic Perspective</title><description>Machine Learning: a Probabilistic Perspective</description><link>http://www.cs.ubc.ca/~murphyk/MLbook/</link><dc:creator>muehlburger</dc:creator><dc:date>2014-01-31T11:21:43+01:00</dc:date><dc:subject>books machine-learning machinelearning </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;Machine Learning: a Probabilistic Perspective&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/books"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machine-learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machinelearning"/></rdf:Bag></taxo:topics></item><item rdf:about="http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html"><title>Machine Learning Cheat Sheet (for scikit-learn) </title><description></description><link>http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html</link><dc:creator>muehlburger</dc:creator><dc:date>2013-01-27T11:57:23+01:00</dc:date><dc:subject>cheatsheet machinelearning peedaboo programming python scikitlearn </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; 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