1. Statistical Learning Introduction: Data Mining Process and Modeling Examples Data Mining Process

2. We can see associations between customer type and fraudulent behavior. Are they legitimate? Data leakage? Our goal is to build model to predict fraud in advance

3. Targeting, Sales force mgmt. Business problem definition Wallet / opportunity estimation Modeling problem definition Quantile est., Latent variable est. Statistical problem definition Quantile est., Graphical model Modeling methodology design Programming, Simulation, IBM Wallets Model generation & validation OnTarget, MAP Implementation & application development Project evolution and relevance to our course Outside scope Keep in mind This is our domain!

4. Predict whether someone will have a heart attack on the basis of demographic, diet and clinical measurements

5. ESL Chap1 - Introduction Identify the risk factors for prostate cancer (lpsa), based on clinical and demographic variables.

6. Classify a recorded phoneme, based on a log-periodogram. A restricted model (red) does much better than an unrestricted one (jumpy black)

7. Customize an email spam detection system. X = which words appear and how much Y = Spam or not?

8. Identify the numbers in a handwritten zip code, from a digitized image X = color of each pixel Y = which digit is it?

9. Classify a tissue sample into one of several cancer classes, based on a gene expression profile. X = expression levels of genes Y = which cancer?

10. Classify the pixels in a LANDSAT image, according to usage: Y = {red soil, cotton, vegetation stubble, mixture, gray soil, damp gray soil, very damp gray soil} X = values of pixels in several wavelength bands

12. October 2006 Announcement of the NETFLIX Competition USAToday headline: “Netflix offers $1 million prize for better movie recommendations” Details: Beat NETFLIX current recommender model ‘Cinematch’ by 10% based on absolute rating error prior to 2011 $50K for the annual progress price (relative to baseline) Data contains a subset of 100 million movie ratings from NETFLIX including 480,189 users and 17,770 movies Performance is evaluated on holdout movies-users pairs NETFLIX competition has attracted 45878 contestants on 37660 teams from 180 different countries Tens of thousands of valid submissions from thousands of teams Conclusion: in 2009, an international team attained the goal and won the prize! More later…

13. 451 3 2 4 All movies (80K) All users (6.8 M) NETFLIX Competition Data 17K Selection unclear 480 K At least 20 Ratings by end 2005 100 M ratings Data Overview: NETFLIX Internet Movie Data Base Fields Title Year Actors Awards Revenue …

14. 17K movies Training Data Movie Arrival 1998 Time 2005 User Arrival 45? 3 2 ? Qualifier Dataset 3M NETFLIX data generation process

15. Netflix and us We will hopefully hear a talk about Netflix from the winner and also work with the data throughout our course: We will have a modeling challenge in our course which will use the Netflix data. The winners will get a grade boost! The $1M was won in 2009 by a collaboration of several leading teams –The strongest team, which won both yearly $50K prizes, was founded at AT&T, with an Israeli participant (Yehuda Koren) –Yehuda was one of the major driving forces on the final winning team –He is now back in Yahoo! Research Israel, and will give us a talk! While I was at IBM Research, our team won a related competition in KDD-Cup 2007 (same data, more “standard” modeling tasks) –We may have a “case study” lecture on that as well