The course is divided into two parts: nonlinear optimization and linear optimization.  

In the first part of the course you will discover basics of unconstrained optimization in the convex and nonconvex cases, necessary and sufficient conditions for optimality, gradient descent, Newton method, quasi Newton methods, nonlinear least squares, basics on constrained optimization, KKT and brief introduction to stochastic optimization for machine learning.