2. Learning from Data

3. Learning
sensors
actuators
environment
agent ?
As an agent interacts with the world, it should learn about its environment

4. APPLE
APPLE
BANANA
BANANA
APPLE
Supervised learning: given labeled data…

5. Supervised Learning APPLE or BANANA?
…learn to classify (label) new example

6. Unsupervised Learning
Unsupervised learning: Given unlabeled data…

7. Unsupervised Learning
…put examples into groups (Clustering)…

8. Unsupervised Learning?
Given new example, put it into a group.

9. Focus on Supervised Learning first… Given previous data, how can we “learn” to classify new data?

10. APPLE
APPLE
BANANA
BANANA
APPLE or BANANA?
APPLE

11. Training
Training Set

12. Training
Training Set
Extract
features/ labels

13. Training
Training Set
Extract
features/ labels
Train

14. Learned model/ Classifier
Training
Learned model/ Classifier
Training Set
Extract
features/ labels
Train

15. Learned model/ Classifier
Training
Learned model/ Classifier
Training Set
Extract
features/ labels
Train
Classifying
Instance/Example

16. Learned model/ Classifier
Training
Learned model/ Classifier
Training Set
Extract
features/ labels
Train
Classifying
Instance/Example
Extract
features

17. Training Classifying Learned model/ Classifier Training Set Extract
features/ labels
Train
Classifying
Learned model/ Classifier
Instance/Example
Extract
features

18. Training Classifying Learned model/ Classifier Training Set Extract
features/ labels
Train
Classifying
Learned model/ Classifier
Label
Instance/Example
Extract
features

19. Inductive Learning
Generalize (learn) from a training set to make predictions on future test sets.

20. Inductive Learning
Generalize (learn) from a training set to make predictions on future test sets.
Supervised Learning:
Training data is a set of (x, y) pairs
x: input example/instance
y: output/label
Learn an unknown function f such that f(x)=y

21. Inductive Learning
Generalize (learn) from a training set to make predictions on future test sets.
Supervised Learning:
Training data is a set of (x, y) pairs
x: input example/instance
y: output/label
Learn an unknown function f(x)=y
x represented by D-dimensional feature vector x = < x1 , x2 , x3 ,…, xD >
Each dimension is a feature or attribute

22. Some examples image classification text classification
does the image contain a person? apple? banana?
text classification
is this a good/bad review?
is this article about sports or politics?
is this spam?
character recognition
is this set of scribbles an ‘a’, ‘b’, ‘c’, …
credit card transactions
fraud or not?
Many problems!!!

23. Another example Database of 20,000 images of handwritten digits,
each labeled by a human
This is perhaps the most well-studied data set in ML… handwritten digits, part of zipcodes scanned off envelopes by the post office.
Use these to train a classifier which will label digit-images

24. Another example Database of 20,000 images of handwritten digits,
each labeled by a human
This is perhaps the most well-studied data set in ML… handwritten digits, part of zipcodes scanned off envelopes by the post office.
Each image is of size 28x28 pixels.
Labels are 0-9.
Features?

25. Another example Database of 20,000 images of handwritten digits,
each labeled by a human
This is perhaps the most well-studied data set in ML… handwritten digits, part of zipcodes scanned off envelopes by the post office.
Features are RGB values for each pixel
28x28 features,
values ranging from 0 to 255

26. Wait for a Table?
An intelligent agent that tells us if we should wait for a table at a restaurant How to apply supervised learning here?

27. Wait for a Table?
An intelligent agent that tells us if we should wait for a table at a restaurant How to apply supervised learning here? What features should we consider?

28. Wait for a table? Alternate restaurant nearby? Comfy bar to wait at?
Is it Friday?
How hungry are we?
How full is it?
How expensive is it?
What’s the weather like?
Do we have reservations?
Type of food?
Estimated wait time?
Features/Attributes

29. Wait for a Table?

30. Wait for a Table? How to organize/visualize this data?
(Note that each attribute has a finite set of possibilities)

31. Alternate Restaurant. T or F Bar. T or F How full
Alternate Restaurant? T or F Bar? T or F How full? Some, Full, pr None Prince? $, $$, $$$, or $$$$ Estimated Wait Time: 0-10, 10-30, 30-60, >60

32. All examples with
Patrons=None were No
Patrons = Some were Yes
Examples with
Patrons = Full depended
on other features

33. Decision Trees

34. How to classify new example?
All examples with
Patrons=None were No
Patrons = Some were Yes
Examples with
Patrons = Full depended
on other features

35. How to classify new example?
All examples with
Patrons=None were No
Patrons = Some were Yes
Examples with
Patrons = Full depended
on other features

36. Classifying a New Example