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probabilistic graphical models - principles and techniques (2009).pdf

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probabilistic graphical models - principles and techniques (2009).pdf
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programming collective intelligence - building smart web 2.0 applications (2007).pdf
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pattern recognition and machine learning (2007).pdf
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statistical learning theory (1998).pdf
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pattern recognition (4th, 2008).pdf
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neural networks for applied sciences and engineering (2006).pdf
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mining the social web - analyzing data from facebook, twitter, linkedin and other social media sites (2011).pdf
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machine learning for hackers (2012).pdf
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machine learning - an algorithmic perspective (2009).pdf
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machine learning (1997).pdf
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learning with kernels (2002).pdf
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learning deep architectures for AI (2009).pdf
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large-scale inference - empirical bayes methods for estimation, testing, and prediction (2010).pdf
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kernel methods for pattern analysis (2004).pdf
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introduction to machine learning (smola, 2010).pdf
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introduction to machine learning (alpaydin, 2009).pdf
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information theory, inference and learning algorithms (2003).pdf
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gaussian processes for machine learning (2005).pdf
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ensemble methods in data mining (2010).pdf
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elements of statistical learning (2008).pdf
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