Yearly Archives: 2017

LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?

Robot at a Neural Information Processing Systems 2017 lecture.

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LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference? Episode Summary: This 69th episode of Learning Machines 101 provides a short overview of the 2017 Neural Information Processing Systems conference with a focus on the development of methods for teaching learning machines rather than simply training them on examples. In addition, a book review of… Read More »

LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms

Robot skiing on a mountain with snow and thinking about the length of his skis.

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LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms Episode Summary: This 68th episode of Learning Machines 101 discusses a broad class of unsupervised, supervised, and reinforcement machine learning algorithms which iteratively update their parameter vector by adding a perturbation based upon all of the training data. This process is repeated, making… 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 »

LM101-065: How to Design Gradient Descent Learning Machines (Rerun)

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LM101-065: How to Design Gradient Descent Learning Machines (Rerun) Episode Summary: In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of deep learning and neural network learning algorithms. Show Notes: Hello everyone! Welcome to the sixteenth podcast in the podcast series Learning Machines 101. In this series… Read More »

LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun)

Monte Carlo Markov Chain Genetic Algorithms for Model Selection

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LM101-064: Stochastic Model Search and Selection with Genetic Algorithms (Rerun) Episode Summary: In this episode we explore the concept of evolutionary learning machines. That is, learning machines that reproduce themselves in the hopes of evolving into more intelligent and smarter learning machines. This is a rerun of Episode 24. Show Notes: Hello everyone! Welcome to the twenty-fourth podcast in… 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 »

LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine

Example of a deep learning neural network implementing a value function for playing checkers.

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LM101-062: How to Transform a Supervised Learning Machine into a Value Function Reinforcement Learning Machine Episode Summary: This 62nd episode of Learning Machines 101 discusses how to design reinforcement learning machines using your knowledge of how to build supervised learning machines! Specifically, we focus on Value Function Reinforcement Learning Machines which estimate the unobservable total penalty associated with… Read More »

LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN)

Robot participates as an audience member of the conference.

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LM101-061: What happened at the Reinforcement Learning Tutorial? (RERUN) Episode Summary: This is the third of a short subsequence of podcasts providing a summary of events associated with Dr. Golden’s recent visit to the 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode reviews and discusses… Read More »

LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms

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LM101-060: How to Monitor Machine Learning Algorithms using Anomaly Detection Machine Learning Algorithms Episode Summary: This 60th episode of Learning Machines 101 discusses how one can use novelty detection or anomaly detection machine learning algorithms to monitor the performance of other machine learning algorithms deployed in real world environments. The episode is based upon a review of a… Read More »