The document discusses supervised learning in artificial intelligence and machine learning, introducing key concepts such as learning algorithms, models, and data fitting. It provides examples of various models used for predicting outcomes related to laundry cleanliness based on data inputs like washing time and load weight. Additionally, it mentions feature extraction and demonstrates the use of the scikit-learn library.

1. An Introduction To
Supervised Learning

2. Disclaimer!

3. Artificial Intelligence
Machine Learning …
Supervised Learning ...

4. Learning
Algorithm
A( , “Sports”)
B( , “Politics”)
C( , “Business”)
Model
(Function)
“Business”
P
fit() predict()
…

5. Our
Learning
Algorithm
Our
Model
(Function)
“Clean”
(10, “Not Clean”)
(120, “Clean”)
(30, “Not Clean”)
...
105
Minutes in
Washing Machine
Cleanliness

6. Our
Learning
Algorithm
Our
Model
(Function)
1
(10, 0)
(120, 1)
(30, 0)
...
105
Minutes in
Washing Machine
Cleanliness

7. Our
Learning
Algorithm
y
x
Minutes in
Washing Machine
Cleanliness
(10, 0)
(120, 1)
(30, 0)
...

8. Our
Learning
Algorithm
y
x
Minutes in
Washing Machine
Cleanliness
(10, 0)
(120, 1)
(30, 0)
...

9. 1. Sort data
2. Scan from left
looking for change
in label
3. Set value of “a”
y
x
Minutes in
Washing Machine
Cleanliness
(10, 0)
(120, 1)
(30, 0)
...

10. 1. Sort data
2. Scan from left
looking for change
in label
3. Set value of “a”
y
x(10, 0)
(120, 1)
(30, 0)
...
fit() predict()

11. Laundry Cleanliness (Data)

12. Laundry Cleanliness (Data & Model)

13. Satisfaction From Allowance (Data)

14. Satisfaction From Allowance (Data & Model)

15. Satisfaction From Allowance (Data & Model)

16. Number of Facebook Friends till Platform is
Attractive (Data)

17. Number of Facebook Friends till Platform is
Attractive (Data & Model)

18. Average Temperature for Successful Grape
Growing (Data)

19. Average Temperature for Successful Grape
Growing (Data & Model)
✗
✓

20. Learning Algorithms
Have Limited Expressibility

21. Learning
Algorithm
A( , “Sports”)
B( , “Politics”)
C( , “Business”)
Model
(Function)
“Business”, “Politics”, “Business”, ...
A
fit() predict()
…
Evaluation
B C, , , ...
Evaluate

22. Training vs Testing
B “Politics”
C “Business”
D “Business”
E “Sports”
F “Politics”
G “Politics”
H “Business”
A “Sports”

23. Training vs Testing
B “Politics”
C “Business”
D “Business”
E “Sports”
F “Politics”
G “Politics”
H “Business”
A “Sports”
fit()
predict()
“Sports”
“Politics”
“Business”
Evaluate
0.67

24. Over-fitting
(On to the whiteboard!)

25. Learning
Algorithm
Model
(Function)
1
(10, 0)
(120, 1)
(30, 0)
...
105
Minutes in
Washing Machine
Cleanliness

26. Learning
Algorithm
Model
(Function)
1
([10, 1.4], 0)
([120, 3], 1)
([30, 0.5], 1)
...
[105, 1.6]
Minutes in
Washing Machine
Cleanliness
Weight of Load

27. 2D-Laundry Cleanliness (Data)

28. 2D-Laundry Cleanliness
(Linear Model)

29. 2D-Laundry Cleanliness
(QDA Model)

30. 2D-Laundry Cleanliness
(Decision Tree Model)

31. 2D-Laundry Cleanliness
(Random Forest Model)

32. 2D-Laundry Cleanliness
(K-Nearest Neighbors Model)

33. 2D-Laundry Cleanliness
(RBF SVM Model)

34. Learning
Algorithm
Model
(Function)
1
([10, 1.4,40, “Samsung”, ...], 0)
([120, 3, 50, “AEG”, ...], 1)
([30, 0.5, 30, “Siemens”, ...], 1)
...
[105, 1.6, 40, “AEG”, ...]
Minutes in
Washing Machine
Cleanliness
Weight of Load
Temperature
Washing Machine
Brand

35. Feature Extraction
A [124, 2, 40, 30, 12, ...]
Feature
Extractor

36. And now,
a demo with scikit-learn.