In this talk, we’ll see how recommendation systems are created from data. What’s the algorithm? What’s the evaluation method? What’s the optimization procedure? When does it converge? We’ll talk about parallelizing in order to scale up to “big data” size via the MapReduce framework. Finally, we’ll think about priors and how they are overloaded. Content from this talk draws from chapters in Doing Data Science contributed by David Crawshaw and Matt Gattis.

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Bio, etc…