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    LM101-008: How to Represent Beliefs Using Probability Theory

    LM101-008: How to Represent Beliefs Using Probability Theory

    FromLearning Machines 101

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    LM101-008: How to Represent Beliefs Using Probability Theory

    FromLearning Machines 101

    ratings:
    Length:
    31 minutes
    Released:
    Sep 3, 2014
    Format:
    Podcast episode

    Description

    Episode Summary: This episode focusses upon how an intelligent system can represent beliefs about its environment using fuzzy measure theory. Probability theory is introduced as a special case of fuzzy measure theory which is consistent with classical laws of logical inference.
    Released:
    Sep 3, 2014
    Format:
    Podcast episode

    Titles in the series (85)

    Smart machines based upon the principles of artificial intelligence and machine learning are now prevalent in our everyday life. For example, artificially intelligent systems recognize our voices, sort our pictures, make purchasing suggestions, and can automatically fly planes and drive cars. In this podcast series, we examine such questions such as: How do these devices work? Where do they come from? And how can we make them even smarter and more human-like? These are the questions which will be addressed in the podcast series Learning Machines 101.