Introduces Support Vector Machines, covering Hinge Loss, hyperplane separation, and non-linear classification using kernels.
Explores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.
Covers the Perceptron algorithm and its application to binary classification problems, including the Pocket Perceptron algorithm.
Introduces the basics of machine learning, covering supervised classification, logistic regression, and maximizing the margin.
Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.
Covers Kernel Methods in Machine Learning, focusing on overfitting, model selection, cross-validation, regularization, kernel functions, and SVM.
Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.
