1. Bayesian Learning 1 of (probably) 2

2. Administrivia Readings 1 back today Good job, overall Watch your spelling/grammar! Nice analyses, though Possible fruit for final proj? HW2 assigned today Due Oct 12 (2 weeks) Do start early...

3. ML trivia of the day... Which data mining techniques [have] you used in a successfully deployed application? http://www.kdnuggets.com/

4. Bayesian Classification

5. Assumptions “ ‘Assume’ makes an ass out of ‘U’ and ‘me’” Bull**** Assumptions about data are unavoidable Learn faster/better when you know (assume) more about data Decision tree: Axis orthagonality; accuracy cost (0/1 loss) k-NN: Distance function/metric; accuracy cost LSE: Linear separator; squared error cost

6. Assumptions SVMs Linear separator High-dim projection (kernel function) Generalized inner product/cosine Max margin cost function

7. Specifying assumptions Bayesian learning assumes: Data were generated by some stochastic process Can write down (some) mathematical form for that process CDF/PDF/PMF Mathematical form needs to be parameterized Have some “prior beliefs” about those params Essentially, an attempt to make assumptions explicit and to divorce them from learning algorithm In practice, not a single learning algorithm, but a recipe for generating problem-specific algs.

8. Example F ={height, weight} C ={male, female} Q1: Any guesses about individual distributions of height/weight by class? What probability function (PDF)? Q2: What about the joint distribution? Q3: What about the means of each? Reasonable guess for the upper/lower bounds on the means?

9. Some actual data* * Actual synthesized data, anyway...

10. General idea Find probability distribution that describes classes of data Find decision surface in terms of those probability distributions

11. H/W data as PDFs

12. Or, if you prefer...

13. General idea Find probability distribution that describes classes of data Find decision surface in terms of those probability distributions What would be a good rule?

14. The Bayes optimal decision For 0/1 loss (accuracy), is provable that optimal decision is: Equivalently, it’s sometimes useful to use log odds ratio test:

15. Bayes decisions in pictures x1 f(x1) f(x1|c2) f(x1|c1) c1 c2 c1

16. Bayesian learning process So where do the probability distributions come from? The art of Bayesian data modeling is: Deciding what probability models to use Figuring out how to find the parameters In Bayesian learning, the “learning” is (almost) all in finding the parameters

17. Back to the H/W data

18. Gaussian (a.k.a. normal or bell curve) is a reasonable assumption for this data Other distributions better for other data Can make reasonable guesses about means Probably not -3 kg or 2 million lightyears Assumptions like these are called Model assumptions (Gaussian) Parameter priors (means) How do we incorporate these into learning? Prior knowledge

19. 5 minutes of math... Our friend the Gaussian distribution 1n 1-dimension: Mean: Std deviation: Both parameters scalar Usually, we talk about variance rather than std dev:

20. 5 minutes of math... In d dimensions: Where: Mean vector: Covariance matrix: Determinant of covariance:

21. Exercise: For the 1-d Gaussian: Given two classes, with means and and std devs and Find a description of the decision point if the std devs are the same, but diff means And if means are the same, but std devs are diff For the d-dim Gaussian, What shapes are the isopotentials? Why? Repeat above exercise for d-dim Gaussian