Title: Mathematical Optimization for Engineers

Location: US

Company: RWTH Aachen University

About this course:
Become an Expert in Optimization with our exciting MOOC!
Today, for almost every product on the market and almost every service offered, some form of optimization has played a role in their design.
However, optimization is not a button-press technology. To apply it successfully, one needs expertise in formulating the problem, selecting and tuning the solution algorithm and finally, checking the results. We have designed this course to make you such an expert.
This course is useful to students of all engineering fields. The mathematical and computational concepts that you will learn here have application in machine learning, operations research, signal and image processing, control, robotics and design to name a few.
We will start with the standard unconstrained problems, linear problems and general nonlinear constrained problems. We will then move to more specialized topics including:

Mixed-integer problems

Global optimization for non-convex problems

Optimal control problems

Machine learning for optimization

Optimization under uncertainty

Students will learn to implement and solve optimization problems in Python through the practical exercises.
Enroll now to enhance your skills in optimization and apply them to real-world challenges!

Mathematical definitions of objective function, degrees of freedom, constraints and optimal solution

Mathematical as well as intuitive understanding of optimality conditions

Different optimization formulations (unconstrained v/s constrained; linear v/s nonlinear; mixed-integer v/s continuous; time-continuous or dynamic; optimization under uncertainty)

Fundamentals of the solution methods for each these formulations

Optimization with machine learning embedded

Hands-on training in implementing and solving optimization problems in Python, as exercises

Learn the mathematical and computational basics for applying optimization successfully. Master the different formulations and the important concepts behind their solution methods. Learn to implement and solve optimization problems in Python through the practical exercises.

Duration: 8 weeks

Skills:
- Algorithms
- Basic Math
- Image Processing
- Machine Learning
- Mathematical Optimization
- Operations Research
- Optimal Control
- Python (Programming Language)
- Robotics

Curriculum:
- Mathematical definitions of objective function, degrees of freedom, constraints and optimal solution
- Mathematical as well as intuitive understanding of optimality conditions
- Different optimization formulations (unconstrained v/s constrained; linear v/s nonlinear; mixed-integer v/s continuous; time-continuous or dynamic; optimization under uncertainty)
- Fundamentals of the solution methods for each these formulations
- Optimization with machine learning embedded

- Hands-on training in implementing and solving optimization problems in Python, as exercises