Presentation made for the training on machine learning at mroads

1. Support Vector
Machines
An Optimization Problem

2. What is an Optimization
Problem??

3. Let’s Learn This Like a Five Year Old
We have 2 colors of balls on the
table that we want to separate.

4. We get a stick and put it on the table,
this works pretty well right?

5. Some villain comes and places more
balls on the table, it kind of works but
one of the balls is on the wrong side
and there is probably a better place
to put the stick now.

6. SVMs try to put the stick in
the best possible place by
having as big a gap on either
side of the stick as possible.

7. Now when the villain returns the
stick is still in a pretty good spot.

8. There is another trick in the SVM
toolbox that is even more
important. Say the villain has seen
how good you are with a stick so
he gives you a new challenge.

9. There’s no stick in the world that
will let you split those balls well,
so what do you do? You flip the
table of course! Throwing the
balls into the air. Then, with your
pro ninja skills, you grab a sheet
of paper and slip it between the
balls.

10. Now, looking at the balls from where
the villain is standing, the balls will
look split by some curvy line.

11. Let’s Visualise Those Other Dimensions

12. Let’s Go Little Deeper
Support Vectors are attempting to find a
hyperplane that divides the two classes with
the largest margin. The support vectors are
the points which fall within this margin.
Here margin means the distance from the
closest elements to the hyperplane .

13. One Doubt??
How is it different from
1. Neural Nets
2. Linear Regression (On Linearly Separable data)

14. Differs Based on points influencing
optimality
All points
● Linear Regression
● Neural Nets (These can find the hyperplane but not optimal one)
only “difficult points” close to decision boundary
• Support vector machines

15. A Hyperplane

16. Proof
Use Board

17. Non Linearly Separable

18. The idea is to gain linearly
separation by mapping the
data to a higher dimensional
space.
Transformation to separate

19. Check out IDIOTS GUIDE TO SVM.
What is phi What are Kernels How to transform ????

20. Let’s Start Practicing
it !!!!!