Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.
Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
Explores the use of Gaussian Mixture Models for transitioning from clustering to classification, covering binary classification, parameter estimation, and optimal Bayes classifier.