32 min listen
LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)
ratings:
Length:
35 minutes
Released:
Jan 26, 2015
Format:
Podcast episode
Description
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.
Please visit: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!
Please visit: www.learningmachines101.com to obtain transcripts of this podcast and download free machine learning software!
Released:
Jan 26, 2015
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