Category Archives: Monte Carlo Markov Chain

LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets

Self-driving car doesn't see the difference between hitting a baby carriage or hitting a garbage can because it does not have common sense.

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LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets Episode Summary: In this podcast, we provide some insights into the complexity of common sense. First, we discuss the importance of building common sense into learning machines. Second, we discuss how first-order logic can be used to represent common sense knowledge. Third, we describe… Read More »

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 »

LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)

Robot trying to solve a probabilistic constraint satisfaction problem.

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LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun) Episode Summary: In this episode 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… Read More »

LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization)

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LM101-053: How to Enhance Learning Machines with Swarm Intelligence (Particle Swarm Optimization) Episode Summary: In this 53rd episode of Learning Machines 101, we introduce the concept of a Swarm Intelligence with respect to Particle Swarm Optimization Algorithms. The essential idea of “Swarm Intelligence” is that you have a group of individual entities which behave in a coordinated manner… Read More »