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Hello and welcome.

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My name is Stephen Dembo, and I'm excited to be your instructor for this introduction course on data

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fusion with the Linnear Common Fiodor.

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So why the focus on data fusion and common filtering data fusion is an amazing tool that is used in

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pretty much every modern piece of technology that involves any kind of sensing measurement or automation.

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The Common Filter is one of the most widely used methods for data fusion.

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And by understanding this process, you will more easily understand more complicated data fusion methods.

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The common field can be difficult for beginners to comprehend how a filter works and how to apply the

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concepts in practice, and particularly for their situation.

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And lastly, so you don't waste time trying to solve debug problems that would be easily avoided with

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the knowledge of this course, you can become a subject matter expert indictee.

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Fusion and common filtering discourse has been made for a wide range of people all the way from university

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students and independent learners to working engineers and scientists, engineering professionals who

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want to brush up on the math and skills related to data fusion and common filtering software developers

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who wish to understand the basic concepts behind data fusion to aid in their implementation and support

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of developing data fusion code.

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This course is going to cover basic probability and random variables and how to represent uncertainty

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using probabilistic concepts.

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It's going to cover dynamic systems and it's based representations based squiz estimation, linear coming,

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filtering, including the theory, implementation and use cases and all the theoretical explanation

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and analysis is done using Python and simulations to teach the concepts.

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So by the end of this course, you know how to probabilistic express uncertainty using probability distributions,

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you know how to convert differential systems into space representations, you know how to simulate and

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describe these states space dynamic systems, you know how to use these squares estimation to solve

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estimation problems.

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You know how to use the linear computer to solve the optimal estimation problems.

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You'll be able to drive the system matrixes for the common Philidor in general, for any problems that

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you come up against, you know how to optimally churn the linear filter for best performance.

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And also you'll know how to implement the common filter in Python.

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So you got a lot of things to learn, so you better kick off and start this learning journey.