Nancy will present for the first hour, and Mu will continue after a short break.
Nancy will give an overview of the topics to be covered during the thematic program, along with some background on how and why these topics were chosen, describe other topics that are important, but not covered, and give a summary of various musings on "Big Data" that she has been collecting since starting on this adventure.
Mu will cover some basics about supervised learning, in particular, regression (e.g., linear regression, nearest neighbors, kernel regression), classification (e.g., logistic regression, linear discriminant analysis, naive Bayes, neural network), and fundamental principles such as the bias-variance trade-off and the curse of dimensionality.
Nancy will give an overview of the topics to be covered during the thematic program, along with some background on how and why these topics were chosen, describe other topics that are important, but not covered, and give a summary of various musings on "Big Data" that she has been collecting since starting on this adventure.
Mu will cover some basics about supervised learning, in particular, regression (e.g., linear regression, nearest neighbors, kernel regression), classification (e.g., logistic regression, linear discriminant analysis, naive Bayes, neural network), and fundamental principles such as the bias-variance trade-off and the curse of dimensionality.
(NR & MZ)