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In the last several decades, thousands of machine learning algorithms have been developed. Very often, the selection of an algorithm to solve a particular problem is driven more by the data scientist's familiarity with a small subset of available algorithms, than optimizing for predictive power or operational constraints. This is unsurprising: Newcomers to machine learning and veteran data scientists alike, may be overwhelmed by the multitude of machine learning algorithms and where and how it is most appropriate to use them.
In this webinar, Daniel Emaasit will introduce Model-Based Machine Learning (MBML), an approach to machine learning which addresses these challenges. Daniel will discuss the various uses of MBML, from tasks such as classification, to regression and clustering, and how it allows data scientists to address the uncretainty inherent to real-world machine learning applications. Daniel will demonstrate how to implement MBML in a probabilistic programming language called Stan, using the RStan package. At the end of webinar, attendees will have the knowledge to build their own custom probabilistic models, learning their parameters from data.
Daniel Emaasit is a Ph.D Student of Transportation Engineering at UNLV, where his research interests involve the development of probabilistic machine learning methods for high-dimensional data, with applications to urban mobility, transport planning, highway safety, & traffic operations.