Alexander Mitsos & his team
Description
Course comprising primers on various types of optimization: LP, NLP, MIP, dynamic programming, ML, stochastic optimization.
Short Summary
This is an introductory but technical course to start with optimization when you have the basic mathematical background (ability to solve linear systems of equations, understand what a matrix is). You will gently be introduced to formulating different optimization models (e.g., unconstrained/constrained, linear/nonlinear, mixed-integer/continuous, dynamic optimization, optimization under uncertainty). The basic solution techniques for such models are covered as well as basic implementation excercises.
When & Why?
You want to learn the basics of modeling and algorithms in optimization with mathematical rigor AND you can put 6-8 hours for 8 weeks in (or equivalently, e.g.3-4 hours for 16 weeks) AND basic knowledge of linear algebra, ability to solve simple, basic python programming tasks.
How to?
Self-paced online course - paid variant with examination and certificate.
Review: what is good & less good?
The didactic quality of introducing the most relevant topics in optimization is world-class. The obvious flip side that comes with this: Because of the broad range of topics, every topic really only scratches the surface. If you get excited about a particular topic, the course might leave you hungry for more without delivering.
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