1. Linear Methods, cont’d; SVMs intro

2. Straw poll Which would you rather do first? Unsupervised learning Clustering Structure of data Scientific discovery (genomics, taxonomy, etc.) Reinforcement learning Control Robot navigation Learning behavior

3. Reminder... Finally, can write: Want the “best” set of w : the weights that minimize the above Q: how do you find the minimum of a function w.r.t. some parameter?

4. Reminder... Derive the vector derivative expressions: Find an expression for the minimum squared error weight vector, w, in the loss function:

5. Solution to LSE regression Differentiating the loss function...

6. LSE followup The quantity is called a Gram matrix and is positive semidefinite and symmetric The quantity is the pseudoinverse of X The complete “learning algorithm” is 2 whole lines of Matlab code So far, we have a regressor -- estimates a real valued y for each X Can convert to a classifier by assigning y=+1 or -1 to binary class training data Q: How do you handle non-binary data?

7. Handling non-binary data DTs and k -NN can handle multi-class data Linear discriminants (& many other) learners only work on binary 3 ways to “hack” binary classifiers to p -ary data: 1 against many: Train p classifiers to recognize “class 1 vs anything else”; “class 2 vs everything else”... Cheap, easy May drastically unbalance the classes for each classifier What if two classifiers make diff predictions?

8. Multiclass trouble

9. Handling non-binary data All against all: Train O(p^2) classifiers, one for each pair of classes Run every test point through all classifiers Majority vote for final classifier More stable than 1 vs many Lot more overhead, esp for large p Data may be more balanced Each classifier trained on very small part of data

10. Handling non-binary data Coding theory approach Given p classes, choose b≥lg(p) Assign each class a b -bit “code word” Train one classifier for each bit Apply each classifier to a test instance => new code => reconstruct class x1x2x3y green3.2-9apple yello w 1.80.7lemon yello w 6.9-3banana red0.8 grape green3.40.9pear x1x2x3 y1y1 y2y2 y3 green3.2-9000 yellow1.80.7001 yellow6.9-3010 red0.8 011 green3.40.9100

11. Support Vector Machines

12. Linear separators are nice... but what if your data looks like this:

13. Linearly nonseparable data 2 possibilities: Use nonlinear separators (diff hypothesis space) Possibly intersection of multiple linear separators, etc. (E.g., decision tree)

14. Linearly nonseparable data 2 possibilities: Use nonlinear separators (diff hypothesis space) Possibly intersection of multiple linear separators, etc. (E.g., decision tree) Change the data Nonlinear projection of data These turn out to be flip sides of each other Easier to think about (do math for) 1st case

15. Nonlinear data projection Suppose you have a “projection function”: Original feature space “Projected” space Usually Do learning w/ linear model in Ex:

16. Common projections Degree- k polynomials: Fourier expansions:

17. Example nonlinear surfaces SVM images from lecture notes by S. Dreiseitl

18. Example nonlinear surfaces SVM images from lecture notes by S. Dreiseitl

19. Example nonlinear surfaces SVM images from lecture notes by S. Dreiseitl

20. Example nonlinear surfaces Text SVM images from lecture notes by S. Dreiseitl

21. The catch... How many dimensions does have? For degree- k polynomial expansions: E.g., for k =4, d =256 (16x16 images), Yike! For “radial basis functions”,