Machine Learning From Data, Rensselaer Fall 2020.
Professor Malik Magdon-Ismail talks about the support vector machine and the optimal hyperplane that is most robust to input noise. We introduce the optimal hyperplane (maximum margin) and develop the algorithm to find it based on quadratic programming.
This is the twenty-third lecture in a "theory" course focusing on the foundations of learning, as well as some of the more advanced techniques like support vector machines and neural (deep) networks that are used in practice.
Level of the course: Advanced undergraduate, beginning graduate. Knowledge of probability, linear algebra, and calculus is helpful.
Material is from e-Chapter 8 of "Learning From Data", amlbook.com, 2012.
