Interpolation and curve fitting techniques are widely-used by scientists and engineers. Why? Well, experiments generate data and it's necessary to find a way to model this data mathematically. Curve fitting helps us do that!

This course covers interpolation and curve fitting techniques typically found in an undergraduate-level Numerical Methods course.

MATLAB will be used to implement the methods on the computer.

What we'll cover:

• Least squares regression

• Polynomial regression

• Lagrange interpolating polynomials

• Newton's divided differences

• Linear splines

• Quadratic splines

• Cubic splines

• MATLAB implementation of the methods

What comes with the course:

• Downloadable outline of notes (.pdf file) to help you follow along with the lectures and keep you engaged

• 14 downloadable MATLAB .m files of all codes used in the course

• Easy to follow lecture videos

After this course you'll be able to generate your own curve fits for experimental data as well as know how to properly interpolate to get the best estimates.

If you're taking a Numerical Methods course at a University, I've got you covered! We'll work through examples by hand as well as using MATLAB. This way you'll be prepared if you get an exam problem you have to complete by hand.

If you're looking for a way to improve your coding skills this is a great course for that too! We'll cover lots of algorithms that'll use different coding concepts like if-elseif statements and for loops. So, if you want more practice with programming in MATLAB this course will definitely give you that experience.