Supervised Machine Learning Review
Review key concepts in supervised machine learning relevant to deep neural networks in this 47-minute lecture. Explore the statistical machine learning framework, principles for selecting loss functions, and the bias-variance tradeoff. Delve into regression, classification, terminology, and the statistical framework. Learn about choosing loss functions, linear regression, binary classification, and cross-entropy loss. Examine the relationship between model complexity and training error, concluding with insights on the surprising double-descent behavior in highly overparameterized neural networks. Access accompanying lecture notes for a comprehensive understanding of the material presented by Paul Hand in Northeastern University's CS 7150 Summer 2020 Deep Learning course.