Convex Sets and Functions - Analyzing and prooving convexity for sets and functions.
Optimization Algorithms - Implementation and analysis of the Gradient Descent and Newton algorithms.
KKT Conditions and Projections - Solving optimization problems with constraints.
Projected Gradient Algorithms - Devolpment of projected (accelerated) Gradient algorithms.
Logistic Regression - Optimization of the LR model using GD, Accelerated GD and Stochastic GD.
Support Vector Machines - Hard-SVMs using CVX, Soft-SVMs using Stochastic Sub-GD and SVMs with Kernels.
Linear Program and Barrier Functions - Comparison of the interior point method and the primal-dual algorithm, using the logarithmic barrier function.