Hello and welcome.
My name is Stephen Dembo, and I'm excited to be your instructor for this introduction course on data
fusion with the Linnear Common Fiodor.
So why the focus on data fusion and common filtering data fusion is an amazing tool that is used in
pretty much every modern piece of technology that involves any kind of sensing measurement or automation.
The Common Filter is one of the most widely used methods for data fusion.
And by understanding this process, you will more easily understand more complicated data fusion methods.
The common field can be difficult for beginners to comprehend how a filter works and how to apply the
concepts in practice, and particularly for their situation.
And lastly, so you don't waste time trying to solve debug problems that would be easily avoided with
the knowledge of this course, you can become a subject matter expert indictee.
Fusion and common filtering discourse has been made for a wide range of people all the way from university
students and independent learners to working engineers and scientists, engineering professionals who
want to brush up on the math and skills related to data fusion and common filtering software developers
who wish to understand the basic concepts behind data fusion to aid in their implementation and support
of developing data fusion code.
This course is going to cover basic probability and random variables and how to represent uncertainty
using probabilistic concepts.
It's going to cover dynamic systems and it's based representations based squiz estimation, linear coming,
filtering, including the theory, implementation and use cases and all the theoretical explanation
and analysis is done using Python and simulations to teach the concepts.
So by the end of this course, you know how to probabilistic express uncertainty using probability distributions,
you know how to convert differential systems into space representations, you know how to simulate and
describe these states space dynamic systems, you know how to use these squares estimation to solve
estimation problems.
You know how to use the linear computer to solve the optimal estimation problems.
You'll be able to drive the system matrixes for the common Philidor in general, for any problems that
you come up against, you know how to optimally churn the linear filter for best performance.
And also you'll know how to implement the common filter in Python.
So you got a lot of things to learn, so you better kick off and start this learning journey.