Description 
This is primarily an introductory course on convex optimization. The focus however is on topics which might be useful for machine learning and
computer vision researchers. Accordingly, some advanced/specialized
topics are included: 1. Theory
• Convex Analysis: Convex Sets, Convex Functions, Calculus of convex functions
• Optimality of Convex Programs: 1st order nec. and suff. conditions, KKT conditions
• Duality: Lagrange and Conic duality
2. Standard Convex Programs and Applications
• Linear and Quadratic Programs
• Conic Programs: QCQPs, SOCPs, SDPs.
3. Optimization Techniques
• Smooth Problems: (proj.) Gradient descent, Nesterov`s accelerated method, Newton`s methods
• Nonsmooth Problems: (proj.) Subgradient descent
• Special topics: Active set and cutting planes methods
