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. Simulator: http://www.cs.cmu.edu/~zhuxj/courseproject/knndemo/KNN.html

10. Simple Nearest Neighbor Nearest Neighbor Applied Pattern Nearest Neighbor Nearest 3 Neighbors

11. Other Nearest Neighbor Nearness as pixel difference:

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

13. Spam Filter Is a web page "spam"?

14. Spam Filter Is a web page "spam"?

15. Spam Filter Is a web page "spam"? How do we decide the questions???

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

17. Learning a Decision Tree http://aispace.org/dTree/

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

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

20. Trick 3: Neural Networks Biologically inspired computation

21. Neural Networks Biologically inspired computation

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

23. Neural Networks

24. Sunglasses Network Image recognition network:

25. Sunglasses Network Image recognition network:

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

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

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

29. Learning http://aispace.org/neural/

30. Result One neuron's weights

31. Making it all worth it http://www.cs.cmu.edu/~tom7/mario/