1. How do you get here? https://www.youtube.com/watch?v=dk3oc1Hr62g

2. Pattern Recognition & Machine Learning

3. Patterns
Humans are excellent at recognizing patterns

4. Patterns
Even if we can't explain how we do it…

5. Trick 1: Nearest Neighbor
Task : predict what houses are most likely to donate to an election

6. Nearest Neighbor
Task : predict what houses are most likely to donate to an election
Know some voter
registrations

7. Nearest Neighbor
Task : predict what houses are most likely to donate to an election
What should we predict for the ? marks

8. Nearest Neighbor
Task : predict what houses are most likely to donate to an election
Should we consider more than one neighbor?

9. Other Nearest Neighbor
Nearness as pixel difference:

10. Trick 2: Decision Trees Sequnce of choices to make a decision
Do I need an umbrella?

11. Learning a Decision Tree
Is an important?

12. Machine Learning
Machine Learning : Build a general algorithm to LEARN specific patterns

13. Learning a Decision Tree

14. Human Involvement
Still need to determine possible questions, things to look at

15. Human Involvement
Still need to determine possible questions, things to look at
What should we look at for these???

16. Trick 3: Neural Networks
Biologically inspired computation

17. Neural Networks
Biologically inspired computation

18. Neural Networks
A simple "take umbrella" network:

19. Neural Networks

20. Sunglasses Network
Image recognition network:

21. Sunglasses Network
Image recognition network:

22. Enhanced Neurons
Signals can be any value 0-1

23. Enhanced Neurons
Signals can be any value 0-1
Inputs can be weighted

24. Enhanced Neurons Signals can be any value 0-1 Inputs can be weighted
Threshold function is not all or nothing
Produces values 0-1

25. Learning Neural network learns via training
Guess for lots of known examples
Update weights based on success/failure No
Yes
Yes No No
Yes

26. Samples

27. Result
One neuron's weights:

28. Other samples