This is the final course of this series. Because this course focuses on the theories behind the algorithms introduced in the first and second courses. There are many math prove and derive, so I’m not going to explain the details. I’ll mention the key points I learned from this course. If you want to know the theory details, you can check the course page. The course is free.
Learned Things
The theory is the keystone of an algorithm. A breakthrough theory can change the field. If the simplex method is without the theory behind this, the operations research field can’t become widely used nowadays.
Knowing the theory behind the algorithm can let us know why the model can’t work and we can formulate a better model to improve our result.
The operations research theory can be used in machine learning algorithms. The machine learning algorithm is an optimization model. Operations Research is the foundation of Statistics and Machine learning. When I finished a machine learning course provided by MIT, I already noticed that. Now, I’m more confident it is certainly true.
The criteria of a good model are (1). Make sense. (2). Solvable
Summary
After taking the three courses, I reviewed my knowledge of mathematical programming and learned more details about the algorithm and theory. In the future, I can use this knowledge and techniques to solve practical problems with a high-quality model.
My certification:
