Tag Archives: Expectation Maximization

LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)

Contrastive Divergence Learning in pattern recognition machines as a neurally plausible theory of why people dream.

LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun) Episode Summary: 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. Specifically, the important machine learning method… Read More »

LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine

policy gradient reinforcement learning illustration

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LM101-063: How to Transform a Supervised Learning Machine into a Policy Gradient Reinforcement Learning Machine Episode Summary: This 63rd episode of Learning Machines 101 discusses how to build reinforcement learning machines which become smarter with experience but do not use this acquired knowledge to modify their actions and behaviors. This episode explains how to build reinforcement learning machines… Read More »

LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun)

Contrastive Divergence Learning in pattern recognition machines as a neurally plausible theory of why people dream.

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LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22) 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 week will digress with a rerun of Episode 22 which… Read More »

LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems (Expectation Maximization)

Contrastive Divergence Learning in pattern recognition machines as a neurally plausible theory of why people dream.

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Episode Summary: 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. Show Notes: Hello everyone! Welcome to the twenty-second podcast in the podcast series Learning Machines 101. In this… Read More »