In this episode we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated probabilistic constraints among a collection of variables. The goal of the inference process is to infer the most probable values of the unobservable variables given the observable variables. Concepts of Markov Random Fields and Monte Carlo Markov Chain methods are discussed. For additional details and technical notes, please visit the website: <a href="http://www.learningmachines101.com">www.learningmachines101.com</a>
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LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis

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 that will be addressed in this podcast series!