Decision Surfaces. Support Vector Machines Lecture
Nowadays, Artificial Intelligence drives scientific and economic growth worldwide. This is largely due to advances in Machine Learning (ML). Its applications span and revolutionize almost every human activity:
-Autonomous Systems (cars, drones, vessels),
-Media Content and Art Creation (including fake data creation/detection), Social Media Analytics,
-Medical Imaging and Diagnosis,
-Financial Engineering (forecasting and analytics), Big Data Analytics,
-Broadcasting, Internet and Communications,
-Robotics/Control
-Intelligent Human-Machine Interaction, Anthropocentric (human-centered) Computing,
-Smart Cities/Buildings and Assisted living.
-Scientific Modeling and Analytics.

This lecture overviews Decision Surfaces and, in particular, Support Vector Machines that have many applications in Machine Learning and Pattern Recognition. It covers the following topics in detail: Decision surfaces. Hyperplanes. Non-linear Decision Surfaces. Quadratic (2nd degree polynomial) surfaces, Hyperellipsoid/Hyperparaboloid. Support Vector Machines, Margin Maximization, Lagrangian Primal/Dual Problem, Kernel SVM.