Yearly Archives: 2016

LM101-059: How to Properly Introduce a Neural Network

LM101-059: How to Properly Introduce a Neural Network Episode Summary: I discuss the concept of a “neural network” by providing some examples of recent successes in neural network machine learning algorithms and providing a historical perspective on the evolution of the neural network concept from its biological origins. Show Notes: Hello everyone! Welcome to the fifty-ninth podcast in… Read More »

LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis

LM101-058: How to Identify Hallucinating Learning Machines using Specification Analysis Episode Summary: In this 58th episode of Learning Machines 101, I’ll be discussing an important new scientific breakthrough published just last week for the first time in the journal Econometrics  in the special issue on model misspecification titled “Generalized Information Matrix Tests for Detecting Model Misspecification”. The article… Read More »

LM101-057: How to Catch Spammers using Spectral Clustering

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

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 »

LM101-055: How to Learn Statistical Regularities using MAP and Maximum Likelihood Estimation (Rerun)

LM101-055: How to Learn Statistical Regularities using MAP and ML Estimation Episode Summary: In this rerun of Episode 10, we discuss fundamental principles of learning in statistical environments including the design of learning machines that can use prior knowledge to facilitate and guide the learning of statistical regularities. Show Notes: Hello everyone! Welcome to the tenth podcast in… Read More »

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

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 »

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

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 »

LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear

LM101-052: How to Use the Kernel Trick to Make Hidden Units Disappear Episode Summary: Today, we discuss a simple yet powerful idea which began popular in the machine learning literature in the 1990s which is called “The Kernel Trick”. The basic idea behind “The Kernel Trick” is that an impossible machine learning problem can be transformed into an… Read More »

LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun]

LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun] Episode Summary: In this episode we describe how to download and use free nonlinear machine learning software for implementing a Perceptron learning machine with a single layer of Radial Basis Function hidden units for the purposes of supervised learning. Show Notes: Welcome to the 51st podcast… Read More »

LM101-050: How to Use Linear Regression Software to Make Predictions (RERUN)

LM101-050: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN] Episode Summary: In this episode we describe how to download and use free linear machine learning software to make predictions for classifying flower species using a famous machine learning data set. This is a RERUN of Episode 13. Show Notes: Hello everyone! Welcome to… Read More »

LM101-049: How to Experiment with Lunar Lander Software

LM101-049: How to Experiment with Lunar Lander Software Episode Summary: In this episode we continue the discussion of learning when the actions of the learning machine can alter the characteristics of the learning machine’s statistical environment. We describe how to download free lunar lander software so you can experiment with an autopilot for a lunar lander module that… Read More »

LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun)

LM101-048: How to Build a Lunar Lander Autopilot Learning Machine (Rerun) 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 from… Read More »

LM101-047: How to Build a Support Vector Machine to Classify Patterns (Rerun)

  LM101-047: How To Build a Support Vector Machine to Classify Patterns (Rerun) Episode Summary: In this RERUN of the 32nd episode of Learning Machines 101, we introduce the concept of a Support Vector Machine. We explain how to estimate the parameters of such machines to classify a pattern vector as a member of one of two categories… Read More »

LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)

LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology) Episode Summary: In this episode, we briefly review Item Response Theory and Bayesian Network Theory methods for the assessment and optimization of student learning and then describe a poster presented on the first day of the Neural Information Processing Systems conference in December 2015 in Montreal… Read More »

LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images

LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images Episode Summary: This is the fourth of a short subsequence of podcasts which provides 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… Read More »

LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?

LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference? 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… Read More »

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

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 »