This course is structured in such a way that each of the topics builds on the previous topics to build
up until we get to the final learning outcomes we have covered in the past that the assumed knowledge
for this course is basically probability and uncertainty.
This squares estimation, linear dynamic systems and C++ programming to all these different areas are
assumed knowledge for this course.
And we'll use each of these areas and build upon each of them to build up our different skill sets.
So first, we introduced the linear common filter.
So this takes concepts from probability and uncertainty, least squares estimation and linear dynamic
systems and builds them up into the linear carbon filter.
We'll also look at the implementation of this filter.
So we use our C++ previous knowledge and look at the linear common implementation problem.
Once you've looked at the linear current filter, you then take the concepts and extend them forward
into nonlinear systems using the extended common filter.
And we do the same thing for the implementation was we looked at the extended common filter.
We'll look at the unscented come a filter and the unscented come filter implementation.
Once we've covered the three different types of common foods as shown here, we'll then look at some
additional topics, so we'll look at how to do filtering in the real world.
So what sort of common errors and situations do we have to compensate for in the real world?
Finally, after we've learned all that, will then look at the capstone project, which is taking concepts
about filtering in the real world and generating a common filter based on the final U.K. implementation
to run for the capstone project.
So this capstone project is to implement a filter for a self-driving car, assuming real world data
for you and problems.