Yearly Archives: 2015

LM101-042: What happened at the Monte Carlo Markov Chain Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?

LM101-042: What happened at the Monte Carlo Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference? Episode Summary: This is the second 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… Read More »

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

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

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 »

LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)[Rerun]

LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain) 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… Read More »

LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets (Educational Technology)

LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets Episode Summary: In this episode, we examine the problem of developing an advanced artificially intelligent technology which is capable of tracking knowledge growth in students in real-time, representing the knowledge state of a student a skill profile, and automatically defining the concept of a skill without… Read More »

LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory

LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory Episode Summary: In this episode, we discuss the problem of how to build a smart computerized adaptive testing machine using Item Response Theory (IRT). Suppose that you are teaching a student a particular target set of knowledge. Examples of such situations obviously occur in… Read More »

LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks

LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks Episode Summary: In this episode, we discuss the problem of predicting the future from not only recent events but also from the distant past using Recurrent Neural Networks (RNNs). A example RNN is described which learns to label images with simple sentences. A learning… Read More »

LM101-035: What is a Neural Network and What is a Hot Dog?

Episode Summary: In this episode, we address the important questions of “What is a neural network?” and “What is a hot dog?” by discussing human brains, neural networks that learn to play Atari video games, and rat brain neural networks. Show Notes: Hello everyone! Welcome to the thirty-fifth podcast in the podcast series Learning Machines 101. In this… Read More »

LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]

LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun] Episode Summary: In this episode we describe how to download and use free nonlinear machine learning software which is more advanced than the linear machine software introduced in Episode 13. Show Notes: Welcome to the 34th podcast in the podcast series… Read More »

LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]

LM101-033: 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… Read More »

LM101-032: How To Build a Support Vector Machine to Classify Patterns

LM101-032: How To Build a Support Vector Machine to Classify Patterns Episode Summary: In this 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 as well as identify special… Read More »

LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)

LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN) 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… Read More »

LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)

LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging) Episode Summary: Deep learning machine technology has rapidly developed over the past five years due in part to a variety of factors such as: better technology, convolutional net algorithms, rectified linear units, and a relatively new learning strategy called “dropout” in which hidden… Read More »

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

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 »

LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]

LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN] Episode Summary: In this episode we discuss the problem of how to evaluate the ability of a learning machine to make generalizations and construct abstractions given the learning machine is provided a finite limited collection of experiences. Show Notes: Hello everyone! Welcome to a RERUN of… Read More »

LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]

LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN] Episode Summary: In this podcast episode, we discuss the design of statistical learning machines which can make inferences about rare and unseen events using prior knowledge. Show Notes: Hello everyone! Welcome to a RERUN of the 11th podcast in the podcast series Learning Machines 101. In this… Read More »

LM101-026: How to Learn Statistical Regularities (Rerun)

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 the… Read More »

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

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 »

LM101-024: How to Use Genetic Algorithms to Breed Learning Machines (Stochastic Model Search and Selection)

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. [wpdm_package id=’1503′] Show Notes: Hello everyone! Welcome to the twenty-fourth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is… Read More »

LM101-023: How to Build a Deep Learning Machine (Function Approximation)

Episode Summary: In this episode we discuss how to design and build “Deep Learning Machines” which can autonomously discover useful ways to represent knowledge of the world. Show Notes: Hello everyone! Welcome to the twenty-third podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts of artificial intelligence… Read More »

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

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 »

LM101-021: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain)

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 unobservable variables given the observable variables. Show Notes: Hello everyone! Welcome… Read More »

LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions

Episode Summary: In this episode we describe how to download and use free nonlinear machine learning software which is more advanced than the linear machine software introduced in Episode 13. Show Notes: Hello everyone! Welcome to the twentieth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important concepts… Read More »