- What is Machine Learning? - How is Machine Learning different from traditional programming? - How does it work? - How to use it? - Tools - Resources

1. Machine Learning
Narong Intiruk
Data Scientist, T2P Co,.Ltd.
Begin with

2. Agenda
• What is Machine Learning?
• How is Machine Learning different from traditional
programming?
• How does it work?
• How to use it?
• Tools
• Resources

3. What is Machine Learning?
“Field of study that gives computers the ability to learn
without being explicitly programmed”
- Arthur Samuel, 1959

4. What is Machine Learning?
Learning
Algorithm
"A computer program is said to learn from experience E with respect to
some class of tasks T and performance measure P if its performance at
tasks in T, as measured by P, improves with experience E."
- Tom M. Mitchell
Cat / Not Cat
this is a cat
E
T
Cat -> Cat : 80%
P

5. How is Machine Learning different from traditional
programming?

6. How is Machine Learning different from traditional
programming?
if (eyes == 2) & (legs == 4) & (tail == 1)… then
print “Cat”
“ Cat ”
Traditional programming
Input
Program
Computer
Output
- eyes
- legs
- tail
- …..
- …..

7. How is Machine Learning different from traditional
programming?
if (eyes == 2) & (legs == 4) & (tail == 1)… then
print “Cat”
“ Cat ”
Traditional programming
Input
Program
Computer
Output
- eyes
- legs
- tail
- …..
- …..

8. How is Machine Learning different from traditional
programming?
“ Cat ”
Machine Learning
Input
Program
Computer
Output
Cat Recognition
- eyes
- legs
- tail
- …..
- …..

9. How does it work?
Feature
Extraction
Learning
Algorithm
Evaluation
Optimization
Data
Model
Error
Predicted Value = f(X)
We need f that Predicted Value close to True Value
(X)
(f)
Learning Process
Parameters

10. f( )=“ Cat ”
f( )=“ Cat ”
What is f()?
How does it work?

11. Data
Number/Categorical
Age Height Weight Gender
19 160 55 male
25 170 55 male
30 175 60 femal
ImageText
Sound
How does it work?

12. Feature
Extraction
How does it work?
What are the things that should be considered?

13. Feature
Extraction
Good BMI Bad BMI
Weight
(X1)
Height
(X2)
Good BMI
Bad BMI
Height Weight good/bad
160 55 good
170 55 bad
175 60 good
.. .. ..
Features Space
How does it work?

14. Feature
Extraction
How does it work?
Multiple Dimension
Feature

15. Feature
Extraction
How does it work?

16. Feature
Extraction
How does it work?

17. Feature
Extraction
Word Importance
the 0.03
property 0.32
viceroy 0.56
….. …..
How does it work?

18. Feature
Extraction
Principle
Component
Analysis
Dimensionality Reduction
How does it work?

19. Feature
Extraction
Automatic Feature Extraction
Using Deep Learning
http://magizbox.com/training/deep_learning/site/introduction/
How does it work?
Hand-Crafted Feature Extraction

20. Feature
Extraction
Automatic Feature Extraction
by using DNN-Deep Learning
http://magizbox.com/training/deep_learning/site/introduction/
How does it work?
Automatic Feature Extraction

21. Feature
Extraction
Automatic Feature Extraction
by using CNN-Deep Learning
Image
https://devblogs.nvidia.com/parallelforall/deep-learning-nutshell-core-concepts/
How does it work?

22. Feature
Extraction
Automatic Feature Extraction
by using RNN-Deep Learning
Text
How does it work?

23. How does it work?
Learning
Algorithm
Car for achieve the goal!
Framework for learning the target function

24. How does it work?
Learning
Algorithm
Type of Learning

25. How does it work?
Learning
Algorithm
Supervised Learning > Classification
( , “Cat”) Learning
Algorithm
Cat Model “Cat”
Is cat or not?

26. How does it work?
Learning
Algorithm
Supervised Learning > Regression
( , 4.5) Learning
Algorithm
House Price
Model
4.3 M.
What is the price of this house?

27. How does it work?
Learning
Algorithm
Unsupervised Learning
( ) Learning
Algorithm
House
Grouping
Model
What is the group of this house?
Group 1

28. How does it work?
Learning
Algorithm
Unsupervised Learning
Group1
Group2
Group3

29. How does it work?
Learning
Algorithm
Reinforcement Learning
( ) Learning
Algorithm
Flappy Bird
Model
How should I move?
“ Down ”
Reward
(+,-)

30. How does it work?
Learning
Algorithm
Machine Learning Algorithms
https://en.wikipedia.org/wiki/List_of_machine_learning_concepts

31. How does it work?
Learning
Algorithm
Tree Based

32. How does it work?
Learning
Algorithm
Tree Based : Random Forest

33. How does it work?
Learning
Algorithm
Support Vector Machine
3D Features2D Features

34. How does it work?
Learning
Algorithm
Neural Network

35. How does it work?
Learning
Algorithm
Neural Network : Deep Learning
Deep Neural Network (DNN)

36. How does it work?
Learning
Algorithm
Neural Network : Deep Learning
Convolutional Neural Network (CNN)

37. How does it work?
Learning
Algorithm
Neural Network : Deep Learning
Recurrent Neural Network(RNN)

38. How does it work?
Evaluation
Learning
Algorithm
Cat / Not Cat
Model
this is a cat
Cat -> Cat : 200
Cat -> Not Cat : 10
How many is loss?

39. How does it work?
Optimization
Tuning the car’s engine

40. How does it work?
Optimization
Parameters Space (W1,W2)
f(X) = X1W1 + X2W2 +b
J is the cost function of Learning Algorithm

41. How does it work?
Optimization
http://playground.tensorflow.org/
Find the best parameters for the function
bad fair good very good

42. How to use it?
test
trainX
Y

43. How to use it?
Feature matrix and Label vector
1 0.2 0.32 -0.93 0.32
2 -0.17 0.32 0.52 -0.93
3 -0.93 -0.17 0.52 -0.17
4 0.32 0.52 -0.17 -0.93
1
0
0
1
X
Y
}Input Output

44. How to use it?
Feature Extraction : Number/Categorical
no x1 x2 x3 x4
1 0.2 0.32 -0.93 0.32
2 -0.17 0.32 0.52 -0.93
3 -0.93 -0.17 0.52 -0.17
X
}
Age Height Weigh
t
Gender Abnormal
19 160 55 male No
25 170 55 male No
30 120 60 femal Yes
0
0
1
Y
Normal(0)/
Abnormal(1)
Covert categorical to number
and normalize data

45. How to use it?
Feature Extraction : Text -> Binary Vectors
doc1 : You are so cool
doc2 : You are very bad
doc3 : You are very good
word index
you 0
are 1
so 2
cool 3
very 4
bad 5
good 6
corpus dictionary
doc1 : 0 1 2 3
doc2 : 0 1 4 5
doc3 : 0 1 4 6
Feature index
no x0 x1 x2 x3 x4 x5 x6
1 1 1 1 1 0 0 0
2 1 1 0 0 1 1 0
3 1 1 0 0 1 0 1
Binary Feature Vectors

46. How to use it?
Feature Extraction : Text -> TF-IDF Vectors
doc1 : You are so cool
doc2 : You are very bad
doc3 : You are very good
word index
you 0
are 1
so 2
cool 3
very 4
bad 5
good 6
corpus dictionary
doc1 : 0 1 2 3
doc2 : 0 1 4 5
doc3 : 0 1 4 6
Feature index
no x0 x1 x2 x3 x4 x5 x6
1 0.1 0.1 0.3 0.3 0 0 0
2 0.1 0.1 0 0 0.2 0.3 0
3 0.1 0.1 0 0 0.2 0 0.3
TF-IDF Feature Vectors
word index
you 0.1
are 0.1
so 0.3
cool 0.3
very 0.2
bad 0.3
good 0.3
corpus TF-IDF
https://en.wikipedia.org/wiki/Tf%E2%80%93idf

47. How to use it?
Feature Extraction : Text -> Word2Vec
word2Vec model

48. How to use it?
Feature Extraction : Text -> Word2Vec -> Visualize

49. How to use it?
Feature Extraction : Image -> Pixel Vector
gray scale image (MxN) normalized image matrix (MxN)
[0,0,0,..,0.6,0.5,0,0,0,0,0,0,0,0,0……..0]
pixel1
pixelMxN
Pixel Vector

50. How to use it?
Feature Extraction : Image -> Pixel Vector
[0,0,0,..,0.6,0.5,0,0,0,0,0,0,0,0,0……..0]image1
Deep Neural Network

51. How to use it?
Feature Extraction : Image -> raw / gray scale image
raw image
1
2
3
4
5
Convolutional Neural Network
http://cs.stanford.edu/people/karpathy/convnetjs/

52. How to use it?
Model Evaluation
Confusion Matrix

53. How to use it?
Model Evaluation
Confusion Matrix Example
Normal Abnormal
Predict Normal 90 10
Predict Abnormal 10 90
Precision = 90/(90 + 10) = 0.9
Recall = 90/(90 + 10) = 0.9
Acc = (90+90)/(90+10+90+10) = 0.9

54. How to use it?
Model Evaluation : Underfitting and Overfitting
Good on training set but
bad on test set
Bad on training set
and test set
Good on training set
and test set

55. How to use it?
cluster
number
Unsupervised Learning Workflow

56. How to use it?
Evaluation
Sum of squared erros

57. Tools
Data Preparation
Machine Learning
Deep Learning
Keras
Data Visualization
scikit-learn

58. Resources
http://scikit-learn.org/stable/
https://keras.io/
https://www.tensorflow.org/
http://cs.stanford.edu/people/karpathy/convnetjs/
https://www.coursera.org/learn/machine-learning
[DeepLearning.TV]
https://www.youtube.com/channel/UC9OeZkIwhzfv-_Cb7fCikLQ
http://machinelearningmastery.com/introduction-python-deep-learning-library-keras/
[Deep Learning Summer School 2016]
https://www.youtube.com/playlist?list=PLWtzrfzH7gsfxTs8neTRJDXuqAn7qeV4E
http://courses.washington.edu/css490/2012.Winter/lecture_slides/02_math_essentials.pdf
[Deep learning at Oxford 2015]
https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu