The document discusses machine learning and how machines can learn. It provides an example of how a machine can calculate the distance between vectors and group data points. A machine is able to classify new data points by calculating distances to group means and moving points to the closest mean, repeating until group means do not change significantly. This clustering method demonstrates how machines can learn to classify data through an iterative process of calculating distances and grouping similar points.

1. Mujahid Sultan
AIP Tech Trends
Machine Learning

2. The most amazing invention
of the 20th Century was ?

3. The most amazing invention
of the 20th Century was ?
COMPUTER

4. In 21st Century
we will reach
new heights in
technology
which may endanger our
very existence ?

6. Machine Learning is the basis of Artificial intelligence

7. How can we make machines learn.
(with a simple example)
Learning Objectives are:

8. Machine Learning
What is
machine ?

9. Some Basics

10. Vector (x, y)

11. Vector 1 (x1, y1)
Vector 2 (x2, y2)

12. Vector 1 (x1, y1)
Vector 2 (x2, y2)
Can a machine calculate distance between these?

13. Vector 1 (x1, y1)
Vector 2 (x2, y2)
Can a machine calculate distance between these?
(0,0)

14. Vector 1 (x1, y1)
Vector 2 (x2, y2)
(x2-x1)
(y2-y1)
(0,0)
Can a machine calculate distance between these?

15. Vector 1 (x1, y1)
Vector 2 (x2, y2)
(x2-x1)
(y2-y1)
d2 = (x2-x1)2 + (y2-y1)2)
(0,0)
Distance between vectors

16. Vector 1 (a1, b1, ………,n1 )
Vector 2 (a2, b2, ……….., n2)
d2 = (a2-a1)2 + (b2-b1)2 + ……+ + (n2-n1)2)
In higher dimensions:

17. Vector 1 (a1, b1, ………,n1 )
Vector 2 (a2, b2, ……….., n2)
Euclidian Distance
In higher dimensions:

18. Vector 1 (a1, b1, ………,n1 )
Vector 2 (a2, b2, ……….., n2)
Euclidian Distance
In higher dimensions:
Any other distance measures?

19. Vector 1 (a1, b1, ………,n1 )
Vector 2 (a2, b2, ……….., n2)
Correlation
In higher dimensions:

20. Vector 1 (a1, b1, ………,n1 )
Vector 2 (a2, b2, ……….., n2)
Manhattan Distance
In higher dimensions:

21. Vector 1 (Normal)
Vector 2 (Cancerous)
Profile of human genome, high dimensional vector.

22. Vector 1 (Normal)
Vector 2 (Cancerous)

23. Vector 1 (Normal)
Vector 2 (Cancerous)

24. Vector 1 (Normal)
Vector 2 (Cancerous)

25. (Normal)
(Cancerous)
Data Set

26. (Normal)
(Cancerous)
Randomly select two points
in the (domain) space
1
2

27. (Normal)
(Cancerous)
2
1
Calculate distance from 1St point
to each data point

28. (Normal)
(Cancerous)
12
1
Calculate distance from 2nd Point
to each data point

29. (Normal)
(Cancerous)
Move these two points to the mean
of data points closer to each
1
2

30. (Normal)
(Cancerous)
Move these two points to the mean
of data points closer to each
2
1

31. (Normal)
(Cancerous)
Calculate distance from each point and
and move it to the mean of group
2
1

32. (Normal)
(Cancerous)
Calculate distance from each point and
and move it to the mean of group
2
1

33. (Normal)
(Cancerous)
Calculate distance from each point and
and move it to the mean of group
2
1

34. (Normal)
(Cancerous)
Calculate distance from each point and
and move it to the mean of group
Stop when the means are not changing
significantly
2
1

35. (Normal)
(Cancerous)
2
1
Calculate distance from each point and
and move it to the mean of group
Stop when the means are not changing
significantly

36. (Normal)
(Cancerous)
2
1
Calculate distance from each point and
and move it to the mean of group
Stop when the means are not changing
significantly

37. (Normal)
(Cancerous)
2
1
Calculate distance from each point and
and move it to the mean of group
Stop when the means are not changing
significantly

38. (Normal)
(Cancerous)
2
1
Calculate distance from each point and
and move it to the mean of group
Stop when the means are not changing
significantly

39. (Normal)
(Cancerous)
For any new patient,
machine can classify
2
1

40. Today we learned a simple example
how we can make machines learn,
called
Machine Learning which is the
basis of Artificial intelligence