In this episode, we briefly review Item Response Theory and Bayesian Network Theory methods for the assessment and optimization of student learning and then describe a poster presented on the first day of the Neural Information Processing Systems conference in December 2015 in Montreal which describes a Recurrent Neural Network approach for the assessment and optimization of student learning called “Deep Knowledge Tracing”. For more details check out:
<a href="http://www.learningmachines101.com">www.learningmachines101.com</a> and follow us on Twitter at: @lm101talk

LM101-047: How Build a Support Vector Machine to Classify Patterns (Rerun)

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!