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 GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be used in practice. Briefly, AIC and GAIC provide a way of estimating the average prediction error of your learning machine on test data without using test data or cross-validation methods. The GAIC is also called the Takeuchi Information Criterion (TIC).
LM101-046: How to Optimize Student Learning using Recurrent Neural Networks (Educational Technology)
LM101-045: How to Build a Deep Learning Machine for Answering Questions about Images
LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
LM101-043: How to Learn a Monte Carlo Markov Chain to Solve Constraint Satisfaction Problems (Rerun of Episode 22)
LM101-042: What happened at the Monte Carlo Markov Chain (MCMC) Inference Methods Tutorial at the 2015 Neural Information Processing Systems Conference?
LM101-041: What happened at the 2015 Neural Information Processing Systems Deep Learning Tutorial?
LM101-040: How to Build a Search Engine, Automatically Grade Essays, and Identify Synonyms using Latent Semantic Analysis
LM101-039: How to Solve Large Complex Constraint Satisfaction Problems (Monte Carlo Markov Chain and Markov Fields)[Rerun]
LM101-038: How to Model Knowledge Skill Growth Over Time using Bayesian Nets
LM101-037: How to Build a Smart Computerized Adaptive Testing Machine using Item Response Theory
LM101-036: How to Predict the Future from the Distant Past using Recurrent Neural Networks
LM101-035: What is a Neural Network and What is a Hot Dog?
LM101-034: How to Use Nonlinear Machine Learning Software to Make Predictions (Feedforward Perceptrons with Radial Basis Functions)[Rerun]
LM101-033: How to Use Linear Machine Learning Software to Make Predictions (Linear Regression Software)[RERUN]
LM101-032: How To Build a Support Vector Machine to Classify Patterns
LM101-031: How to Analyze and Design Learning Rules using Gradient Descent Methods (RERUN)
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
LM101-029: How to Modernize Deep Learning with Rectilinear units, Convolutional Nets, and Max-Pooling
LM101-028: How to Evaluate the Ability to Generalize from Experience (Cross-Validation Methods)[RERUN]
LM101-027: How to Learn About Rare and Unseen Events (Smoothing Probabilistic Laws)[RERUN]
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