SVM and Multiclass Classification

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Explores linear models for classification, logistic regression, decision boundaries, SVM, multi-class classification, and practical applications.

Covers classification problems and various loss functions used in machine learning.

Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Explains the strategies for multi-class classification and the concept of decision boundaries.

Explores maximizing margins for better classification using support vector machines and the importance of choosing the right parameter.

Covers learning bounds, complexities, growth function, shattering, and VC dimension in binary classifiers.

Covers the basics of support vector machines, logistic regression, decision boundaries, and the k-Nearest Neighbors algorithm.

Covers the history and applications of SVM, as well as the construction of linear classifiers and the concept of classifier margin.

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.