Linear Models: ClassificationExplores linear models for classification, including logistic regression, decision boundaries, and support vector machines.
Multiclass SVMCovers the use of Support Vector Machines for multi-class classification and the importance of support vectors in tightening classification boundaries.
Linear Models for ClassificationExplores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.
Logistic Regression: Part 1Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.
Max-Margin ClassifiersExplores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.
Multiclass ClassificationCovers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.
Support Vector Machines: SVMsExplores Support Vector Machines, covering hard-margin, soft-margin, hinge loss, risks comparison, and the quadratic hinge loss.
Naive Bayes ClassifierIntroduces the Naive Bayes classifier, covering independence assumptions, conditional probabilities, and applications in document classification and medical diagnosis.