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If you need help with your professional engineering problem, or you need to develop new skills in the fields of control, signal processing, embedded systems, programming, optimization, machine learning, robotics, etc., we are here to help. We provide engineering services, as well as tutoring and skill development services. We have significant industry, research, and university-level teaching experience. Describe your problem and we will send you a quote for our services.
Contact: ml.mecheng@gmail.com
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- Buy me a Coffee: www.buymeacoffee.com/Aleksand...
- PayPal: www.paypal.me/AleksandarHaber
- Patreon: www.patreon.com/user?u=320801...
- You Can also press the Thanks KZread Dollar button
It takes a significant amount of time and energy to create these free video tutorials. You can support my efforts in this way:
- Buy me a Coffee: www.buymeacoffee.com/Aleksand...
- PayPal: www.paypal.me/AleksandarHaber
- Patreon: www.patreon.com/user?u=320801...
- You Can also press the Thanks KZread Dollar button
The post accompanying this KZread video is given here:
aleksandarhaber.com/introduct...
The GitHub page with all the codes can be found here:
github.com/AleksandarHaber/Di...
In this video and in the accompanying post, we explain how to implement a recursive least-squares method in Python. The recursive least squares method is the basis for a number of control, machine learning and system identification algorithms. The special emphasis of this video is on the disciplined implementation of the recursive least squares method. Under the term disciplined implementation, we understand an implementation approach that pays special emphasis on code readability and reusability. We explain how to write a Python class that stores and updates covariance matrices and estimates, as well as other quantities necessary for the implementation of the recursive least squares method.