Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.
Explores the use of Gaussian Mixture Models for transitioning from clustering to classification, covering binary classification, parameter estimation, and optimal Bayes classifier.
Covers the use of Support Vector Machines for multi-class classification and the importance of support vectors in tightening classification boundaries.
Explores decision and regression trees, impurity measures, learning algorithms, and implementations, including conditional inference trees and tree pruning.
Explores evaluation protocols in machine learning, including recall, precision, accuracy, and specificity, with real-world examples like COVID-19 testing.
