# Linear Approximation

- Here's an introduction to machine learning.
- If you have a plot of points on a 2D chart.
- You can calculate a best fit line through them.
- This is linear approximation.
- If the line is a good fit, storing the line ax+by=c
is a lot cheaper than storing say 100 points (or even 2).
- Given a y (or an x) you can calculate the x (or y).
- With the points, if you have the y, you can get the x.
- Without the line, how do you get an x if you don't have a
y? (Nearest neighbor?)
- You can use linear regression to solve for a line, or other
more sophisticated methods to derive more complex
functions as approximators.
- One way to do it is to plot the points on a graph, and eye
ball it (people are pretty good at this).
- Another way is to minimize the mean square error.
- If the mean error is small, it's a good fit.
- Look at the simple linear reqression wiki for a formula (that
won't be on the exam).