Explores linear models for classification, including parametric models, regression, and logistic regression, along with model evaluation metrics and maximum margin classifiers.
Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.
Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.
Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.
Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.
