BLiNQ MEDIA Praneeth Vepakomma Senior Data Scientist

1. Generalization in Supervised Machine Learning BLiNQ MEDIA Praneeth Vepakomma Senior Data Scientist

2. Hypothetical Knapsack of Coins: • Copper and Gold Coins • Total number of coins is fixed and is a large sample. • Capture-Recapture • What is the proportion of Gold coins? • Copper and Gold Coins • Total number of coins is variable and is a large sample. • Capture-Recapture • What is the proportion of Gold coins?

3. BASIC ML/STAT TERMINOLOGY:

4. 190 Years after Gauss, the core problem of prediction remains an active problem : Then: Now:

6. 190 Years after Gauss, the core problem of prediction remains an active problem : Find a mapping♯ from the features: is a list of parameters, required to represent the function #Approximation

7. What is Supervised Learning? Loss Function Existing Features Known Labels Assumptions Loss Function Unavailable Features UnknownLabels

8. Evaluating the Learned Function: • Loss Function quantifies the error in the approximation. • Learn a mapping by optimizing the loss. Example:

9. Predictions with varying parameters:

10. Predictions with varying parameters:

11. How do we generalize?

12. Generalization and Predictability Empirical Risk Minimization: • Empirical Risk is the average (expected) loss on seen data. • True Risk is the expected risk on the process generating the X,Y pairs. True Risk Minimization:

14. PARAMETRIC CHARACTERIZATION OF THE MAPPING : • 2d-Linear function: Slope, Intercept • Cubic Spline: Number of knots, Location of Knots • Nearest-Neighbor regression: Number of neighbors • Lasso: L1-L2 Weights • Support Vector Machines: Kernel width, Margin Length • Random Forests: Resampling sample size

19. Long list of available Supervised Learning Techniques. • Most of the techniques have tuning parameters. • We can minimize out-of-sample performance by tuning the technique with optimal parameters. • Tuning can be performed by cross-validation over a discrete grid of parameter combinations.

21. CURSE OF DIMENSIONALITY- Flat World-10D World:

22. CURSE OF DIMENSIONALITY- Flat World-10D World:

23. CURSE OF DIMENSIONALITY- Flat World-10D World:

24. CURSE OF DIMENSIONALITY- Let us validate:

25. Structural Risk Minimization via Regularization:

26. Geneology?

27. Brief Description Technology Overview Hiring (What we’re looking for) http://blinqmedia.com/contact/job-openings/

28. Lets work with Abalone

29. Thank You!