Category Archives: Unsupervised Learning

LM101-070: How to Identify Facial Emotion Expressions Using Stochastic Neighborhood Embedding

Robot looking at a facial image from a security camera.

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LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding Episode Summary: This 70th episode of Learning Machines 101 we discuss how to identify facial emotion expressions in images using an advanced clustering technique called Stochastic Neighborhood Embedding. We discuss the concept of recognizing facial emotions in images including applications to problems such as: improving… Read More »

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

monitor machine learning

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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 »

LM101-057: How to Catch Spammers using Spectral Clustering

harvesting spiders and spammers

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LM101-057: How to Catch Spammers using Spectral Clustering Episode Summary: In this 57th episode, we explain how to use spectral cluster analysis unsupervised machine learning algorithms to catch internet criminals who try to steal your money electronically! Show Notes: Hello everyone! Welcome to the fifty-seventh podcast in the podcast series Learning Machines 101. In this series of podcasts… Read More »

LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications

Probabilistic Topic Models

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LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications Episode Summary: In this episode we discuss Latent Semantic Indexing type machine learning algorithms which have a probabilistic interpretation. We explain why such a probabilistic interpretation is important and discuss how such algorithms can be used in the design of document retrieval… Read More »

LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)

Mother and android looking at a park scene and learning about "dogs".

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LM101-054: How to Build Search Engine and Recommender Systems using Latent Semantic Analysis (RERUN)   Episode Summary: In this episode we explain how to build a search engine, automatically grade essays, and identify synonyms using Latent Semantic Analysis. Preamble: Welcome to the 54th Episode of Learning Machines 101 titled “How to Build a Search Engine, Automatically Grade Essays,… 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 »

LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?

Neural Information Processing Systems Conference and Morning Tutorial on Deep Learning

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LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial? Episode Summary: This is the first of a short subsequence of podcasts which provides a summary of events at the recent 2015 Neural Information Processing Systems Conference. This is one of the top conferences in the field of Machine Learning. This episode introduces the Neural… Read More »

LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis

Mother and android looking at a park scene and learning about "dogs".

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LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis Episode Summary: In this episode we explain how to build a search engine, automatically grade essays, and identify synonyms using Latent Semantic Analysis. Show Notes: Hello everyone! Welcome to the fortieth podcast in the podcast series Learning Machines 101. In this… Read More »

LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling

Deep learning neural network is projected on projection screen.

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LM101-029: How to Modernize Deep Learning  with Rectilinear units,  Convolutional Nets, and Max-Pooling Episode Summary This podcast discusses the topics of rectilinear units, convolutional nets, and max-pooling relevant to deep learning which were inspired by my recent visit to the 3rd International Conference on Learning Representations (May 7-9, 2015) in San Diego. Specifically, commonly used techniques shared by… Read More »

LM101-025: How to Build a Lunar Lander Autopilot Learning Machine (adaptive control)

An illustration of a robot landing a lunar lander on the moon.

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LM101-025: How to Build a Lunar Lander Autopilot Learning Machine (adaptive control) Episode Summary: In this episode we consider the problem of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We illustrate the solution to this problem by designing an autopilot for a lunar lander module that learns… 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 »

LM101-016: How to Analyze and Design Learning Rules using Gradient Descent Methods

Android robot skis downhill.

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Episode Summary: In this episode we introduce the concept of gradient descent which is the fundamental principle underlying learning in the majority of machine learning algorithms. Show Notes: Hello everyone! Welcome to the sixteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts of artificial intelligence and… Read More »