20 min listen
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems
ratings:
Length:
27 minutes
Released:
Feb 10, 2015
Format:
Podcast episode
Description
In this episode we discuss how to learn to solve constraint satisfaction inference problems. The goal of the inference process is to infer the most probable values for unobservable variables. These constraints, however, can be learned from experience. At the end of the episode, we discuss one (unproven) theory from the field of neuroscience that our "dreams" are actually neural simulations of variations of events we have experienced during the day and "unlearning" of these dreams helps us to organize our memory!
Visit us at: www.learningmachines101.com to obtain additional references, make suggestions regarding topics for future podcast episodes by joining the learning machines 101 community, and download free machine learning software!
Visit us at: www.learningmachines101.com to obtain additional references, make suggestions regarding topics for future podcast episodes by joining the learning machines 101 community, and download free machine learning software!
Released:
Feb 10, 2015
Format:
Podcast episode
Titles in the series (85)
LM101-003: How to Represent Knowledge using Logical Rules: Episode Summary: In this episode we will learn how to use .rules. to represent knowledge. We discuss how this works in practice and we explain how these ideas are implemented in a special architecture called the production system. by Learning Machines 101