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Aired 11 months ago 24:14
LM101-077: How to Choose the Best Model using BIC
In this 77th episode of www.learningmachines101.com , we explain the proper semantic interpretation of the Bayesian Information Criterion (BIC) and emphasize how this semantic interpretation is fundamentally different from AIC (Akaike Information Criterion) model selection methods. Briefly, BIC is used to estimate the probability of the training data given the probability model, while AIC is used to estimate out-of-sample prediction error. The probability of the training data given the model is called the â€œmarginal likelihoodâ€.Â Using the marginal likelihood, one can calculate the probability of a model given the training data and then use this analysis to support selecting the most probable model, selecting a model that minimizes expected risk, and support Bayesian model averaging. The assumptions which are required for BIC to be a valid approximation for the probability of the training data given the probability model are also discussed.
Aired 2 years ago 32:03
LM101-070: How to Identify Facial Emotion Expressions in Images Using Stochastic Neighborhood Embedding
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 online communication quality, identifying suspicious individuals such as terrorists using video cameras, improving lie detector tests, improving athletic performance by providing emotion feedback, and designing smart advertising which can look at the customerâ€™s face to determine if they are bored or interested and dynamically adapt the advertising accordingly. To address this problem we review clustering algorithm methods including K-means clustering, Linear Discriminant Analysis, Spectral Clustering, and the relatively new technique of Stochastic Neighborhood Embedding (SNE) clustering. At the end of this podcast we provide a brief review of the classic machine learning text by Christopher Bishop titled â€œPattern Recognition and Machine Learningâ€. Make sure to visit: www.learningmachines101.com to obtain free transcripts of this podcast and important supplemental reference materials!
Aired 9 months ago 18:44
Ep. 77 | Five Customer data analytics questions that Modern marketers should be able to answer.
This week Ambition Data, CEO Allison Hartsoe lists five questions modern marketers should be able to answer in the Accelerator. Although the digital sciences are new for marketers, modern marketers need to know how to think in customer data. From personally identifiable information to whether your agency should host your customer data, Allison fills in the gaps that many marketers have missed. In addition, to help companies trust their data and eventually do more for their customers, she throws in a free audit available at ambitiondata.com/freeaudit. Please help us spread the word about building your businessâ€™ customer equity through effective customer analytics. Rate and review the podcast on Apple Podcast, Stitcher, Google Play, Alexaâ€™s TuneIn, iHeartRadio or Spotify. And do tell us what you think by writing Allison at email@example.com or ambitiondata.com. Thanks for listening! Tell a friend! Learn more about your ad choices. Visit megaphone.fm/adchoices