1. Machine Learning
Made Easy with Perl
Lino Ramirez
ramirez@aranducorp.com

2. From Wikipedia

3. From Wikipedia

4. From Wikipedia

5. From Wikipedia
From Wikipedia

6. From Wikipedia
From Wikipedia

8. From Wikipedia

9. $$$

14. 8 years later ...

15. A MSc later ...

16. A PhD later ...

18. Computers
should “empower”
people
not replace them

19. Key Lesson:
“It's all about
empowering people”

20. What do you want to
accomplish?
Machine Learning: 3 phases

21. Well ... There is this guy ...
Machine Learning: 3 phases

22. Video

23. Is that helping?
Video

24. Not really! You should ...
Video

25. Machine Learning:

26. Machine Learning: 3 phases
Preparation
Phase
Modeling Implementation
Phase Phase

27. Machine Learning: Preparation
•Definition
•Gathering
•Analysis
Preparation •Cleaning
Phase •Selection
Modeling Implementation
Phase Phase

28. Machine Learning: Modeling
•Selection
Preparation
•Development
•Evaluation Phase
Modeling Implementation
Phase Phase

29. Machine Learning:
Implementation
•Evaluation
•Implementation
Preparation
Phase
Modeling Implementation
Phase Phase

30. Case Study

31. Case Study
Implemented 100% in
Perl

32. Problem
For a selected group of components
for Nasdaq composite, if I cluster
the financial quotes into three
segments, what are their profiles?

33. Preparation Phase

34. Components for Nasdaq composite?

35. Cluster?

36. Profiles?

39. Gathering data

43. Analysis

44. Analysis
Missing values

45. Analysis
Missing values
Ask customer

46. Analysis
Different range of values

47. Analysis
Different range of values
Scaling

48. Modeling Phase

49. Selection

51. Clustering
Method for dividing data elements into
groups so that items in the same group
are as similar as possible, and items in
different groups are as dissimilar as
possible.

52. Fuzzy C-Means (FCM)
Items are allowed to belong to more
than one group

53. Membership to Cluster Centered in (5.80, 5.65 )
9.5
0.09
9
8.5
0.11 0.01
8
7.5
0.52 0.13
7
6.5
0.98 0.98 0.39
6
5.5
0.94 0.96
5
4.5
4.5 5.5 6.5 7.5 8.5 9.5

54. Membership to Cluster Centered in (5.80, 5.65 )
9.5
0.09
9
8.5
0.11 0.01
8
7.5
0.52 0.13
7
6.5
0.98 0.98 0.39
6
5.5
0.94 0.96
5
4.5
4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5

55. Clusters' Shape
Euclidean distance
Manhattan distance
Tchebychev distance

56. FCM: Step by Step
Initialize partition matrix
stop = false
While ( stop is false )
Compute clusters' centers
Compute new partition matrix
If partition matrices are close to each other
stop = true
Else
partition matrix = new partition matrix

57. Development

59. Installing PDL
Ubuntu or Debian based distribution
$ sudo apt-get install pdl pgplot5
$ sudo apt-get install libterm-readline-gnu-perl
$ sudo cpan
cpan> install PGPLOT

60. Installing PDL (cont.)
Other Linux distribution or Mac OS X:
See:
http://wiki.jach.hawaii.edu/pdl_wiki-bin/wiki/SiteMap
Go to:
Getting Started with PDL
and then to:
Installing PDL the quick and easy way

61. Getting Started with PDL
Project Homepage:
http://pdl.perl.org/
Mailing Lists:
http://pdl.perl.org/maillists/index_en.html
PDL at PerlMonks:
http://perlmonks.org/?node_id=626721

64. n−1
m
uij xj
∑
j=0
vi = , 0≤i≤c−1
n−1
m
uij
∑
j=0

65. 1
u ij= c −1
2 / m −1 
∑  d ij /d kj 
k =0

67. Evaluation

70. Implementation Phase

71. Implementation Phase
Ask your customer

72. Case Study

73. Case Study
Implemented partially
in Perl

74. Problem
For patients with scoliosis,
Can you design a system that using
surface topography could predict
internal spinal deformity sufficiently
well to replace some radiographs?

75. Preparation Phase

76. Scoliosis?

77. Scoliosis

78. Surface Topography?

80. Predict?

81. Gathering data

82. Analysis

83. Additional features?

85. Analysis
Missing values

86. Analysis
Missing values
Ask customer

87. Analysis
Different range of values

88. Analysis
Different range of values
Scaling

89. Modeling Phase

90. Selection

92. SVM vs. Neural Networks

94. Neural Networks Classifier

95. Neural Networks Classifier

96. Neural Networks Classifier

97. Neural Networks Classifier

98. Neural Networks Classifier

99. Support Vector Machines Classifier

100. Support Vector Machines Classifier

101. Development

102. LIBSVM -- A Library for Support
Vector Machines
http://www.csie.ntu.edu.tw/~cjlin/libsvm/
Award winning SVM package distributed
with a “modified BSD license”

104. Development
SVM configuration

105. Development
SVM configuration
Crossvalidation

108. Evaluation

109. Evaluation
How to test the performance?

110. Evaluation
How to test the performance?
Leave one out

111. Evaluation
Results

112. Evaluation
Results
85% determining
need of treatment

113. Evaluation
Results
69% 3-class classification

114. Implementation Phase

115. Implementation Phase
Ask your customer

116. Conclusion

117. Conclusion
Perl excels at empowering
people in all three phases of
the development of a
machine learning application

118. Thank you!
Open for questions now or later
for samples and slides email me at:
ramirez@aranducorp.com
This work is licensed under a Creative Commons
Attribution-NonCommercial-ShareAlike 2.5 License

119. Linear Polynomial
RBF Sigmoid