Learn about advanced concepts in support vector machines (SVMs) in this comprehensive lecture. Explore soft margins, slack variables, and penalty terms to handle non-linearly separable data. Discover how to extend SVMs to multi-class problems using pairwise comparison and continuous ranking techniques. Gain insights into the practical applications of SVMs for complex classification tasks.