Introduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.
Covers clustering, classification, and Support Vector Machine principles, applications, and optimization, including non-linear classification and Gaussian kernel effects.
Covers the use of Support Vector Machines for multi-class classification and the importance of support vectors in tightening classification boundaries.
