This is the presentation I gave at Imperial College London on the topic of Introduction to Machine Learning. The topics include formal definition of Machine Learning, supervised and unsupervised machine learning and an example of each and finally some of the practical implementation of Machine Learning.

1. Big Data & Analytical Unit (BDAU)
Workshop On Machine Learning
Introduction to Machine Learning
Dr. Mian Asbat Ahmad – 3 March 2016

2. Goals of the presentation
• What is Machine Learning
• Types of Machine Learning
• Supervised Learning
• Linear Regression
• Logistic Regression
• Unsupervised Learning
• K-Nearest neighbour
• Applications of Machine Learning
• Practical

3. What is Machine Learning

4. Machine Learning
Definition 1
“Field of study that gives computers the ability to learn without being
explicitly programmed”. Arthur Samuel (1959)

5. Machine Learning
Definition 2
“A computer program is said to learn from Experience E with respect to
some Task T and some Performance measure P, if its performance on T, as
measured by P, improves with experience E”. Tom Mitchell (1998)
Example of Spam Filter
1. Classifying email as spam or ham (Task T)
2. Watching email for spam or ham (Experience E)
3. The number of emails correctly classified as spam or ham (Performance P)
In Machine Learning programs, the Performance P of the program will
improve on Task T with Experience E.

6. Terms of Machine Learning
• Dataset = Training set
• Rows = Training examples (m), m = 14
• Columns = features (n), n = 2
• Input variables/features/predictors/independant (x), x = 1
• Output variable/target variable/Response/dependant (y), y = 1
• (x(i), y(i)) to denote ith training example
• (x(14), y(14)) = (44.99, 996)
• (xj
(i), yj
(i)) to denote ith training example of jth feature
• (x1
(14), y1
(14)) = (44.99, 996)

7. Training
Set
Learning
Algorithm
Hypothesis
h
Output YInput X

8. Main types of Machine Learning

10. Supervised Learning
• Labelled data
• For example (x1, y1), (x2, y2), (x3, y3), (x4, y4), (x5, y5)
• Develop predictive model based on input and output data
• Regression is type of Supervised Learning which deals with continuous data
• Classification is type of Unsupervised Learning which deals with discrete data
Supervised
Learning
Regression
Problem
Classification
Problem

11. Supervised Learning
Linear Prediction or
Linear Regression

12. Linear function
ŷ = mx + b (simple linear regression)
From the figure put the value of m and b
ŷ = 3(x) + 2
Predict y when the value of x is 0
ŷ = 3(0) + 2
ŷ = 0 + 2
ŷ = 2
[http://philschatz.com/precalculus-book/contents/m49324.html]

13. Example of Supervised Learning 1 ( Regression problem)
To predict the average walking
distance of a person per day
with BMI 50 (kg/m2)
from the given data
BMI
Average Distance
Walked Per Day
42.82 1175
30.11 1600
43.24 905
36.44 1229
52.98 650
37.72 1009
48.44 792
46.44 1015
44.99 996
50.98 594
48.2 1022
63.37 196
55.86 429
52.62 522
32.83 1524
53.41 600
51.19 897
44.19 1058
51.73 479
47.34 936
33.57 1203
39.41 987
46.06 1043
42.42 971
43.94 1157
43.83 844
35.43 1165
61.66 351

14. Example of Supervised Learning 2 ( Regression problem)
100
300
500
700
900
1100
1300
1500
1700
25 35 45 55 65 75
AverageDistanceWalkedPerDay
BMI

15. Example of Supervised Learning 3 ( Regression problem)
100
300
500
700
900
1100
1300
1500
1700
25 35 45 55 65 75
AverageDistanceWalkedPerDay
BMI

16. Example of Supervised Learning 4 ( Regression problem)
100
300
500
700
900
1100
1300
1500
1700
25 35 45 55 65 75
AverageDistanceWalkedPerDay
BMI
The answer
is 730 m

17. Supervised Learning
Logistic Regression

18. Example of Supervised Learning 1 (Classification Problem)
Predict if a cancer
of a person with tumour
size 4 and age 40 is
Benign (B) or Malignant (M)
from the given data
Tumor Size Age Cancer
4 52 B
9 62 M
2 31 B
6 60 M
3 38 B
8 72 M
5 20 B
4 35 B
6 47 B
8 49 M
7 50 M
5 32 B
8 65 M
5 42 M
3 27 B
4 21 B
3 20 B
7 67 M
6 50 M
9 57 M
7 60 M

19. Example of Supervised Learning 2 (Classification Problem)
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumour size

20. Example of Supervised Learning 3 (Classification Problem)
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumour size

21. Example of Supervised Learning 4 (Classification Problem)
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumour size
The answer is
Benign

22. Unsupervised Learning
Clustering

23. Unsupervised Learning
• Unlabelled data
• For example (x1, x2, x3, x4, x5)
• Group and interpret data based only on input data
• Find interesting structures in the data
Unsupervised
Learning
Cluterring
Problem

24. Example of Unsupervised Learning 1 (Cluster Problem)
Predict if a cancer
of a person with tumour
size 4 and age 40 is
Benign (B) or Malignant (M)
from the given data.
Tumor Size Age
4 52
9 62
2 31
6 60
3 38
8 72
5 20
4 35
6 47
8 49
7 50
5 32
8 65
5 42
3 27
4 21
3 20
7 67
6 50
9 57
7 60

25. Non labelled data
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumor size

26. Split in to clusters
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumour size

27. Split in to clusters
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumour size
The
answer is
Benign

28. K-Means Algorithm Unsupervised Learning 1
[Prof. Andrew NG]

29. K-Means Algorithm Unsupervised Learning 2
• Take two random points on the data when k = 2
[Prof. Andrew NG]

30. K-Means Algorithm Unsupervised Learning 3
• Find distance of each value from the points
[Prof. Andrew NG]

31. K-Means Algorithm Unsupervised Learning 4
• Take mean of the distance and move the points there
[Prof. Andrew NG]

32. K-Means Algorithm Unsupervised Learning 5
• Find distance of each value from the points
[Prof. Andrew NG]

33. K-Means Algorithm Unsupervised Learning 6
• Take mean of the distance and move the points there
• The data is divided into two clusters
[Prof. Andrew NG]

34. K-Nearest Neighbour (Clustering problem)
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumor size

35. K-Nearest Neighbour (Clustering problem)
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumor size

36. K-Nearest Neighbour (Clustering problem)
0
10
20
30
40
50
60
70
0 1 2 3 4 5 6 7 8 9 10
Age
Tumor size
The answer is
Malignant

37. Applications of Machine Learning

38. Training Machine for detecting faces

39. [Thomas Serre 2012]

40. Training Machine to identify pedestrians
[Thomas Serre 2012]

41. Click to play the video

42. Search Engines (Ranking)

43. https://news.google.com/ (Clustering)

44. https://news.google.com/

45. Genes Clusters
[Su-In Lee, Dana Peer, Aimee Dudley, George Church, Daphne koller]

46. Recommender systems

47. Other applications of Machine Learning
• Data Mining / Data Analysis
• Hand writing recognition
• E.g. Automatic transfer of mail
• Natural language processing
• Analysis of social media (Twitter, Facebook, LinkedIn etc.)
• Credit card legitimate/fraud transaction detection
• Speech Recognition
• Google Now, Siri, Cortana voice recognition
• Recommender System
• Movie recommendations (Netflix)

48. Questions / Comments / Feedback
bdau@imperial.ac.uk
mian.ahmad@imperial.ac.uk