1. Linear methods: Regression & Discrimination Sec 4.6

2. Corrections & Hints The formula for the entropy of a split (Lec 4, Sep 2) is incorrect. Should be: Where is fraction of complete data that ends up in branch i

3. Corrections & Hints Best way to do a random shuffle of a data set? Given: Want something like:

4. Corrections & Hints Usual way is an index shuffle Generate an index vector and a random vector: Then sort on the random vector: Finally, use the “sorted” index row as index into X

5. Corrections & Hints Concavity: The definition given in the homework is buggy The final criterion, is wrong (to see this, consider A more usual definition is: then g() is concave iff: )

6. Corrections & Hints Missing values Both hepatitis and hypothyroid have many missing attribute (feature) values You have 3 choices Drop all points (instances) w/ missing values Report on effects on your data/results Handle missing attr as in Sec 4.7 or 4.1 of text Report (if you can) on effects Find new similar data sets with no missing values Many available in UCI data or other data sets from Weka group

7. Reminders Office hours, Wed 9:00-11:00 AM, FEC345B Homework due at start of next class (Thurs) Late policy (from front material): 1 day late => 50% off I will relax for this assignment. Policy for HW1: 15% per day late (incl weekend days) You may “checkpoint” by handing in what you have on the due date -- penalty points applied only to “delta” E.g. Turn in 60% of proj on Thurs, 40% on Sat before 4:00 (electronic) Total score is 60%+0.7*40%=88% Read cheating policy -- don’t do it!!!

8. Reminder: 1-NN alg Nearest neighbor: find the nearest instance to the query point in feature space, return the class of that instance Simplest possible distance-based classifier With more notation: Distance function is anything appropriate to your data

9. Reminder: k -NN Slight generalization: k -Nearest neighbors ( k - NN) Find k training instances closest to query point Vote among them for label Q: How does this affect system? Q: Why does it work?

10. Exercise Show that k-NN does something reasonable: Assume binary data Let X be query point, X’ be any k -neighbor of X Let p=Pr[Class(X’)==Class(X)] ( p>1/2 ) What is Pr[X receives correct label] ? What happens as k grows? But there are tradeoffs... Let V(k,N)=volume of sphere enclosing k neighbors of X, assuming N points in data set For fixed N, what happens to V(k,N) as k grows? For fixed k, what happens to V(k,N) as N grows? What about radius of V(k,N) ?

11. The volume question Let V(k,N)=volume of sphere enclosing k neighbors of X, assuming N points in data set Assume uniform point distribution Total volume of data is ( 1, w.l.o.g.) So, on average,

12. 1-NN in practice Most common use of 1-nearest neighbor in daily life?

13. 1-NN & speech proc. 1-NN closely related to technique of Vector Quantization (VQ) Used to compress speech data for cell phones CD quality sound: 16 bit/sample 44.1 kHz ⇒ 88.2 kB/sec ⇒ ~705 kbps Telephone (land line) quality: ~10 bit/sample 10 kHz ⇒ ~12.5 kB/sec ⇒ 100 kpbs Cell phones run at ~9600 bps...

14. Speech compression via VQ Speech source Raw audio signal

15. Speech compression via VQ Raw audio “Framed” audio

16. Speech compression via VQ Framed audio Cepstral (~ smoothed frequency) representation

17. Speech compression via VQ Cepstrum Downsampled cepstrum

18. Speech compression via VQ D.S. cepstrum Vector representation Vector quantize (1-NN) Transmitted exemplar (cell centroid)

19. Compression ratio Original: 10 bits 10 kHz; 250 samples/“frame” (25ms/frame) ⇒ 100 kbps; 2500 bits/frame VQ compressed: 40 frames/sec 1 VC centroid/frame ~1M centroids ⇒ ~20 bits/centroid ⇒ ~800 bits/sec!

20. Signal reconstruction Transmitted cell centroid Look up cepstral coefficients Reconstruct cepstrum Convert to audio

21. Not lossless, though!

22. Linear Methods

23. Linear methods Both methods we’ve seen so far: Are classification methods Use intersections of linear boundaries What about regression? What can we do with a single linear surface? Not as much as you’d like...... but still surprisingly a lot Linear regression is the proto-learning method

24. Linear regression prelims Basic idea: assume y is a linear function of X : Our job: find best to fit y “as well as possible” By “as well as possible”, we mean here, minimum squared error:

25. Useful definitions Definition: A trick is a clever mathematical hack Definition: A method is a trick you use more than once

26. A helpful “method” Recall Want to be able to easily write Introduce “pseudo-feature” of X, Now have: And: So: And our “loss function” becomes:

27. Minimizing loss 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?

28. Minimizing loss Back up to the 1-d case Suppose you had the function: And wanted to find w that minimizes l() Std answer: take derivative, set equal to 0, and solve: To be sure of a min, check 2nd derivative too...

29. 5 minutes of math... Some useful linear algebra identities: If A and B are matrices, (for invertible square matrices)

30. 5 minutes of math... What about derivatives of vectors/matrices? There’s more than one kind... For the moment, we’ll need the derivative of a vector function with respect to a vector If x is a vector of variables, y is a vector of constants, and A is a matrix of constants, then:

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