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Probabilistic Knowledge Acquisition and Representation for Natural-language Applications

Probabilistic robotics is a relatively new area of robotics and concerned with robot perception and robot manipulation in the face of uncertainty and incomplete knowledge about the world. In his thorough and concise talk, Daniel Nyga introduces the basics of probability theory. He further shows probabilistic graphical models, including Bayesian networks and Markov Random Fields, explores statistical relational learning using Markov logic networks, and concludes with probabilistic natural language understanding.

Lecture Video
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