In this recorded webcast, Daniel Emaasit introduces model-based machine learning and related concepts, practices and tools such as Bayes' Theorem, probabilistic programming, and RStan.
The field of machine learning has seen the development of thousands of learning algorithms. Typically, scientists choose from these algorithms to solve specific problems. Their choices often being limited by their familiarity with these algorithms. In this classical/traditional framework of machine learning, scientists are constrained to making some assumptions so as to use an existing algorithm. This is in contrast to the model-based machine learning approach which seeks to create a bespoke solution tailored to each new problem.
Want to read more? Daniel recently wrote an Introduction to Model-based Machine Learning.
Apologies for the poor quality of the video. Our webinar platform suffered a service degradation while recording the event. The webinar slides may be found below.
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