32 min listen
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
ratings:
Length:
28 minutes
Released:
Jan 12, 2016
Format:
Podcast episode
Description
Welcome to the 43rd Episode of Learning Machines 101!We are currently presenting a subsequence of episodes covering the events of the recent Neural Information Processing Systems Conference. However, this weekwill digress with a rerun of Episode 22 which nicely complements our previous discussion of the Monte Carlo Markov Chain Algorithm Tutorial. Specifically, today wediscuss the problem of approaches for learning or equivalently parameter estimation in Monte Carlo Markov Chain algorithms. The topics covered in this episode include: What is the pseudolikelihood method and what are its advantages and disadvantages?What is Monte Carlo Expectation Maximization? And...as a bonus prize...a mathematical theory of "dreaming"!!! The current plan is to returnto coverage of the Neural Information Processing Systems Conference in 2 weeks on January 25!! Check out: www.learningmachines101.com for more details!
Released:
Jan 12, 2016
Format:
Podcast episode
Titles in the series (85)
LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test): Episode Summary: This episode we discuss the Turing Test for Artificial Intelligence which is designed to determine if the behavior of a computer is indistinguishable from the behavior of a thinking human being. The chatbot A.L.I.C.E. by Learning Machines 101