One of the goals of this course is to implement practical optimization algorithms in Python.
In this course, we will use Jupyter Notebooks because they allow to combine code, visualizations, and explanations in a single interactive document. Notebooks make it easy to experiment with algorithms, document your process, and share results with others.
LW1: Least Squares Problems
Implement two least squares problems (regression, denoising) and their solutions.
LW2: Descent methods for unconstrained optimization problems
Implement two least squares problems (regression, denoising) and their solutions.