Lectures related to SVM - Principle: Linear Classifiers

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

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Explains how to find the best hyperplane to separate two classes.

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Covers linear models, logistic regression, decision boundaries, k-NN, and practical applications in authorship attribution and image data analysis.