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Logic, Theorem Proving, and Probabilistic Programming

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If you have a question about this talk, please contact Yingzhen Li.

This seminar will give an accessible introduction to logic and declarative programming, along with its theoretical background, practical uses, and relations to machine learning. The talks has three primary aims: (1) to confer key concepts about logical reasoning and how these can be implemented with machines, (2) to empower the audience to solve a wide class of interesting problems using logic programming, (3) to review the current frontier in machine learning, and motivate the search for new fundamental tools (such as probabilistic programming systems).

Along the way, I’ll show how to implement a basic theorem prover, and outline the anatomy of a declarative probabilistic programming language.

Required reading: none.

This talk is part of the Machine Learning Reading Group @ CUED series.

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