Module: Support vector machines

Learning Outcomes

Single Neuron Networks

• Understand how to set up the maximum margin classifier in the linearly separable case
• Extend the linear separable case to non-linearly separable data
• Understand the convex optimization setup, Lagrangians and the dual formulation
• Appreciate the nature of solutions of the SVM:
  • only certain examples (support vectors) determine the solution, identify these from Lagrange multipliers
  • Appreciate that the SVM formulation only deals with pairwise dot products among examples
• Kernel trick that allows us to move from linear to non-linear classification boundaries in the example space

Readings

Experiential Learning

Assessments