This document summarizes the steps and concepts involved in machine learning. It describes a machine learning workshop that will cover an introduction to machine learning, a Python tutorial, and potentially a MATLAB demo. It then discusses the differences between traditional programming and machine learning, noting that machine learning allows computers to learn from data without being explicitly programmed. The rest of the document outlines the basic steps in a machine learning problem: 1) defining the problem, 2) curating and labeling data, 3) selecting a model and algorithm, 4) applying the algorithm to train the model, and 5) evaluating the trained model. It provides examples for each step using an emotion recognition problem from the JAFFE facial image dataset.

1. IDSTEM Workshop:
Basics of Coding and Machine Learning
March 12th, 2022
2:30 – 3:00 pm
Miriam Hewlett

2. Outline
• Introduction to Machine Learning
• Python Tutorial
• MATLAB Demo (time permitting)
IDSTEM Workshop: Basics of Coding and Machine Learning

3. Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
IDSTEM Workshop: Basics of Coding and Machine Learning

4. Traditional
Programming
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
IDSTEM Workshop: Basics of Coding and Machine Learning

5. Traditional
Programming
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
IDSTEM Workshop: Basics of Coding and Machine Learning

6. Traditional
Programming
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
Model
IDSTEM Workshop: Basics of Coding and Machine Learning

7. Traditional
Programming
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
Model
Outputs
IDSTEM Workshop: Basics of Coding and Machine Learning

8. Machine
Learning
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
IDSTEM Workshop: Basics of Coding and Machine Learning

9. Machine
Learning
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
IDSTEM Workshop: Basics of Coding and Machine Learning

10. Machine
Learning
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
Outputs
IDSTEM Workshop: Basics of Coding and Machine Learning

11. Machine
Learning
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
Outputs
Model
IDSTEM Workshop: Basics of Coding and Machine Learning

12. Machine
Learning
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
Outputs
Model
New Data
IDSTEM Workshop: Basics of Coding and Machine Learning

13. Machine
Learning
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
Outputs
Model
New Data
Unknown
Outputs
IDSTEM Workshop: Basics of Coding and Machine Learning

14. Machine
Learning
Machine Learning vs Traditional Programming
“Machine learning is a field of study that gives computers the ability to
learn without being explicitly programmed” – A. Samuel 1959
Data
Outputs
Model
*supervised learning
New Data
Unknown
Outputs
IDSTEM Workshop: Basics of Coding and Machine Learning

15. Why use Machine Learning?
IDSTEM Workshop: Basics of Coding and Machine Learning

16. Why use Machine Learning?
IDSTEM Workshop: Basics of Coding and Machine Learning
Emotion Recognition
JAFFE
Dataset

17. Why use Machine Learning?
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset

18. Why use Machine Learning?
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Khashman, A. (2009) Applications of an
emotional neural network to facial recognition.
Neural Comput & Applic 18: 309-320.
Different images may have
different pose, contrast,
lighting conditions, etc.

19. Why use Machine Learning?
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset Traditional
Programming
Data Model
Outputs

20. Why use Machine Learning?
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset Traditional
Programming
Data Model
Outputs
?

21. Why use Machine Learning?
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset Traditional
Programming
Data Model
Outputs
?
Machine Learning is useful for modelling
complex relationships with no known
governing equation

22. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
4. Apply learning algorithm to
training data to fit model
5. Evaluate trained model on
validation data with known
outputs
IDSTEM Workshop: Basics of Coding and Machine Learning

23. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning

24. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning
Supervised Learning

25. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning
Supervised Learning
Classification
(discrete output)

26. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning
Supervised Learning
Classification
(discrete output)
Regression
(continuous)

27. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning
Supervised Learning
Classification
(discrete output)
Regression
(continuous)
Inputs

28. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning
Supervised Learning
Classification
(discrete output)
Regression
(continuous)
Inputs
Class 1
Class 2

29. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning
Supervised Learning
Classification
(discrete output)
Regression
(continuous)
Inputs
Class 1
Class 2
Class 3

30. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning
Supervised Learning
Classification
(discrete output)
Regression
(continuous)
Inputs
Class 1
Class 2
Class 3
Inputs

31. Machine Learning: Basic Steps
1. Define the problem
IDSTEM Workshop: Basics of Coding and Machine Learning
Supervised Learning
Classification
(discrete output)
Regression
(continuous)
Inputs
Class 1
Class 2
Class 3
Inputs

32. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning

33. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Emotion Recognition
JAFFE
Dataset

34. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset

35. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Data

36. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Data
Training Data
Validation Data
Testing Data

37. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset

38. Class
Neutral
Fear
Happy
Angry
Sad
Surprised
Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset

39. Class
Neutral
Fear
Happy
Angry
Sad
Surprised
Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Representation
1
0
0
0
0
0

40. Class
Neutral
Fear
Happy
Angry
Sad
Surprised
Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Representation
0
0
0
0
1
0

41. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset

42. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
1 2 3 8 4 7 0 2
1 3 5 0 5 1 2 6
9 3 6 2 1 4 6 9
5 3 7 3 0 6 4 2
1 4 5 7 9 2 6 3
2 4 3 8 8 2 6 9
6 3 9 3 6 9 2 7
9 4 6 8 0 2 5 2

43. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
1 2 3 8 4 7 0 2
1 3 5 0 5 1 2 6
9 3 6 2 1 4 6 9
5 3 7 3 0 6 4 2
1 4 5 7 9 2 6 3
2 4 3 8 8 2 6 9
6 3 9 3 6 9 2 7
9 4 6 8 0 2 5 2
1
1
9
5
1
2
6
9
2
3
3
3
…

44. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset

45. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Feature
Extraction

46. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Feature
Extraction

47. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Feature
Extraction
Right
eyebrow
(middle)
Left eye
(inner)
Mouth end (right)

48. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Feature
Extraction
Right
eyebrow
(middle)
Left eye
(inner)
Mouth end (right)
x1
y1
x2
y2
x3
y3
…

49. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
IDSTEM Workshop: Basics of Coding and Machine Learning
Neutral Sad Surprised
Emotion Recognition
JAFFE
Dataset
Feature
Extraction
Right
eyebrow
(middle)
Left eye
(inner)
Mouth end (right)
x1
y1
x2
y2
x3
y3
…
*dimensionality reduction

50. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning

51. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Neural
Networks

52. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Linear
Regression
Neural
Networks

53. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Decision
Trees
Linear
Regression
Neural
Networks

54. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Decision
Trees
Nearest
Neighbor
Linear
Regression
Neural
Networks

55. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Decision
Trees
Nearest
Neighbor
Linear
Regression
Neural
Networks
Support Vector
Machines (SVM)

56. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Input Layer
(features)
Output Layer
(labels)
Hidden
Layer
Neural
Networks

57. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Input Layer
(features)
Output Layer
(labels)
Hidden
Layer
x1
x0
y
y = f(w0 + w1x1 + w2x2)
Neural
Networks

58. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Input Layer
(features)
Output Layer
(labels)
Hidden
Layer
x1
x0
y
y = f(w0 + w1x1 + w2x2)
learned
weights
Neural
Networks

59. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Input Layer
(features)
Output Layer
(labels)
Hidden
Layer
x1
x0
y
y = f(w0 + w1x1 + w2x2)
activation function
(chosen parameter)
Neural
Networks

60. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Learning
Algorithm
Training Data
Model Class
Model Weights

61. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Learning
Algorithm
Training Data
Model Class
Model Weights
W’ = argminW
1
n
!
i=1
n
Li $
yi(W),yi

62. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Learning
Algorithm
Training Data
Model Class
Model Weights
W’ = argminW
1
n
!
i=1
n
Li $
yi(W),yi
weights

63. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Learning
Algorithm
Training Data
Model Class
Model Weights
W’ = argminW
1
n
!
i=1
n
Li $
yi(W),yi
model
prediction
labels
weights
loss function

64. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
IDSTEM Workshop: Basics of Coding and Machine Learning
Learning
Algorithm
Training Data
Model Class
Model Weights
W’ = argminW
1
n
!
i=1
n
Li $
yi(W),yi
model
prediction
labels
weights
loss function
*regularization?

65. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
4. Apply learning algorithm to
training data to fit model
IDSTEM Workshop: Basics of Coding and Machine Learning

66. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
4. Apply learning algorithm to
training data to fit model
IDSTEM Workshop: Basics of Coding and Machine Learning
optimize weights

67. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
4. Apply learning algorithm to
training data to fit model
5. Evaluate trained model on
validation data with known
outputs
IDSTEM Workshop: Basics of Coding and Machine Learning

68. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
4. Apply learning algorithm to
training data to fit model
5. Evaluate trained model on
validation data with known
outputs
IDSTEM Workshop: Basics of Coding and Machine Learning
An estimate of how well our
model will generalize to new data
Training data
Validation data
Model
overfitting

69. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
4. Apply learning algorithm to
training data to fit model
5. Evaluate trained model on
validation data with known
outputs
IDSTEM Workshop: Basics of Coding and Machine Learning

70. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
4. Apply learning algorithm to
training data to fit model
5. Evaluate trained model on
validation data with known
outputs
IDSTEM Workshop: Basics of Coding and Machine Learning
Repeat with different model
classes, parameters, etc.

71. Machine Learning: Basic Steps
1. Define the problem
2. Curate and label the data
3. Select model class,
learning algorithm, loss
function, and other
parameters
4. Apply learning algorithm to
training data to fit model
5. Evaluate trained model on
validation data with known
outputs
IDSTEM Workshop: Basics of Coding and Machine Learning
Repeat with different model
classes, parameters, etc.
Evaluate best performing
model on testing data

72. Machine Learning Limitations
IDSTEM Workshop: Basics of Coding and Machine Learning

73. Machine Learning Limitations
• Large data, computational requirements
• Can produce unexpected results when
evaluated on data under-represented in
the training set
IDSTEM Workshop: Basics of Coding and Machine Learning

74. Machine Learning Limitations
• Large data, computational requirements
• Can produce unexpected results when
evaluated on data under-represented in
the training set
IDSTEM Workshop: Basics of Coding and Machine Learning

75. Questions ?
Tutorial
• Neural networks in Python using Google Colab
• (time-permitting) MATLAB Demo