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).
LM101-086: Ch8: How to Learn the Probability of Infinitely Many Outcomes
LM101-085:Ch7:How to Guarantee your Batch Learning Algorithm Converges
LM101-084: Ch6: How to Analyze the Behavior of Smart Dynamical Systems
LM101-083: Ch5: How to Use Calculus to Design Learning Machines
LM101-082: Ch4: How to Analyze and Design Linear Machines
LM101-081: Ch3: How to Define Machine Learning (or at Least Try)
LM101-080: Ch2: How to Represent Knowledge using Set Theory
LM101-079: Ch1: How to View Learning as Risk Minimization
LM101-078: Ch0: How to Become a Machine Learning Expert
LM101-077: How to Choose the Best Model using BIC
LM101-075: Can computers think? A Mathematician's Response (remix)
LM101-074: How to Represent Knowledge using Logical Rules (remix)
LM101-073: How to Build a Machine that Learns to Play Checkers (remix)
LM101-072: Welcome to the Big Artificial Intelligence Magic Show! (Remix of LM101-001 and LM101-002)
LM101-071: How to Model Common Sense Knowledge using First-Order Logic and Markov Logic Nets
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
LM101-069: What Happened at the 2017 Neural Information Processing Systems Conference?
LM101-068: How to Design Automatic Learning Rate Selection for Gradient Descent Type Machine Learning Algorithms
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
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