Cross-Validation Nonparametric Bootstrap Study of the Linhart-Volkers-Zucchini Out-of_Sample Prediction Error
Cross-Validation Nonparametric Bootstrap Study of the Linhart-Volkers-Zucchini Out-of_Sample Prediction Error170 DownloadsDownload Now!
Search Results for: cross-validation
LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]
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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 »
LM101-012: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)
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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 the twelfth podcast in the podcast series Learning Machines 101. In this series of podcasts… Read More »
LM101-078: Ch0: How to Become a Machine Learning Expert
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This particular podcast is the initial episode in a new special series of episodes designed to provide commentary on a new book that I am in the process of writing. In this episode we discuss books, software, courses, and podcasts designed to help you become a machine learning expert!
LM101-076: How To Choose the Best Model using AIC or GAIC
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Episode Summary: In this episode, we explain the proper semantic interpretation of the Akaike Information Criterion (AIC) and the Generalized Akaike Information Criterion (GAIC) for the purpose of picking the best model for a given set of training data. The precise semantic interpretation of these model selection criteria is provided, explicit assumptions are provided for the AIC and… Read More »
LM101-070: How to Identify Facial Emotion Expressions Using Stochastic Neighborhood Embedding
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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
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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 »
LM101-056: How to Build Generative Latent Probabilistic Topic Models for Search Engine and Recommender System Applications
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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 »
LM101-051: How to Use Radial Basis Function Perceptron Software for Supervised Learning [Rerun]
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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)
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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 »
LM101-047: How to Build a Support Vector Machine to Classify Patterns (Rerun)
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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)
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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 »
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets (Educational Technology)
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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 »
LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]
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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]
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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
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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 »
LM101-020: How to Use Nonlinear Machine Learning Software to Make Predictions
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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 »
LM101-014: How to Build a Machine that Can Do Anything (Function Approximation)
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Episode Summary: In this episode we describe how to build a machine that can take any given pattern of inputs and generate any desired pattern of outputs! Show Notes: Hello everyone! Welcome to the fourteenth 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 »
LM101-013: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)
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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. Show Notes: Hello everyone! Welcome to the thirteenth podcast in the podcast series Learning Machines 101. In this series of podcasts my goal is to discuss important… Read More »