ORIE 4741: Learning with Big Messy Data. Introduction

Transcription

1 ORIE 4741: Learning with Big Messy Data Introduction Professor Udell Operations Research and Information Engineering Cornell September 15, / 33

2 Outline Stories Definitions Kinds of learning Syllabus Logistics 2 / 33

3 Oh, you work with big messy data? Maybe you could help us out...? 3 / 33

4 Demography age gender state income education 29 F CT $53,000 college 57? NY $19,000 high school? M CA $102,000 masters 41 F NV $23,000? / 33

5 Medicine 5 / 33

6 Medicine age gender heart disease statins? 29 F yes no 57? no no? M no no 41 F yes yes / 33

7 Medicine 7 / 33

8 Pollution [Snow, 1854] 8 / 33

9 Pollution location time CO2 O2 O ? ? / 33

10 Marketing 10 / 33

11 Marketing customer product 1 product 2 product 3 1 yes? yes 2 yes yes? 3?? yes / 33

12 Finance 12 / 33

13 Finance ticker t 1 t 2 AAPL GOOG FB / 33

14 14 / 33

15 Data by Volume 15 / 33

16 Outline Stories Definitions Kinds of learning Syllabus Logistics 16 / 33

17 Big NASA, 1997: taxing the capacities of main memory, local disk, and even remote disk 1 image courtesy of Kim IBM 17 / 33

18 Big NASA, 1997: taxing the capacities of main memory, local disk, and even remote disk OED, 2015: data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges 1 image courtesy of Kim IBM 17 / 33

19 Big NASA, 1997: taxing the capacities of main memory, local disk, and even remote disk OED, 2015: data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges 4 Vs: 1 1 image courtesy of Kim IBM 17 / 33

20 Big NASA, 1997: taxing the capacities of main memory, local disk, and even remote disk OED, 2015: data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges 4 Vs: 5th V: value 1 image courtesy of Kim IBM 1 17 / 33

21 Big: our definition Definition An algorithm for big data is one with computational and memory requirements that scale linearly (or nearly linearly) in the size of the data. 18 / 33

22 Big: our definition Definition An algorithm for big data is one with computational and memory requirements that scale linearly (or nearly linearly) in the size of the data. why this definition? independent of hardware business 18 / 33

23 Big: our definition Definition An algorithm for big data is one with computational and memory requirements that scale linearly (or nearly linearly) in the size of the data. why this definition? independent of hardware business if you use only algorithms for big data, then you re working with big data 18 / 33

24 Messy noisy: some (or all) values suffer errors, inaccuracies, or malicious corruption 19 / 33

25 Messy noisy: some (or all) values suffer errors, inaccuracies, or malicious corruption missing: some values are missing, inconsistent, not recorded, or lost 19 / 33

26 Messy noisy: some (or all) values suffer errors, inaccuracies, or malicious corruption missing: some values are missing, inconsistent, not recorded, or lost heterogeneous: values of many different types continuous values (e.g., 4.2, π) discrete values (e.g., 0, 4, 994) nominal values (e.g., apple, banana, pear) ordinal values (e.g., rarely, sometimes, often) graphs or networks (e.g., person 1 is friends with person 2) text (e.g., doctor s note describing symptoms) sets (e.g., items purchased) 19 / 33

27 Learning 20 / 33

28 Learning machine learning? 20 / 33

29 Learning machine learning? human learning? 20 / 33

30 Learning machine learning? human learning? when data is big and messy, machine help is essential for human learning! 20 / 33

31 Data table n examples (patients, respondents, households, assets) d features (tests, questions, sensors, times) a 11 a 1d A =..... a n1 a nd a i is ith row of A: feature vector for ith example a :j is jth column of A: values for jth feature across all examples a ij is jth feature of ith example 21 / 33

32 Outline Stories Definitions Kinds of learning Syllabus Logistics 22 / 33

33 Supervised learning identify one column of data that we want to predict x 11 x 1 d 1 y 1 A = = X y x n1 x n d 1 y n x i X for i = 1,..., n are rows of X y i Y for i = 1,..., n are entries of y 23 / 33

34 Supervised learning identify one column of data that we want to predict x 11 x 1 d 1 y 1 A = = X y x n1 x n d 1 y n x i X for i = 1,..., n are rows of X y i Y for i = 1,..., n are entries of y we believe there is a mapping f : X Y our goal is to learn f y i f (x i ) 23 / 33

35 Example: supervised learning for credit card applications goal: decide which credit card applicants should be approved input space: entries of X R d correspond to fields in credit application e.g., salary, years in residence, outstanding debt, number of credit lines,... output space: Y = {+1, 1} +1 means approve 1 means reject data: D = (x 1, y 1 ),..., (x n, y n ) give credit applications of previous customers, and correct decisions in hindsight 24 / 33

36 Example: supervised learning for credit card applications goal: decide which credit card applicants should be approved input space: entries of X R d correspond to fields in credit application e.g., salary, years in residence, outstanding debt, number of credit lines,... output space: Y = {+1, 1} +1 means approve 1 means reject data: D = (x 1, y 1 ),..., (x n, y n ) give credit applications of previous customers, and correct decisions in hindsight noise? 24 / 33

37 Exercise: formalizing real problems identify a prediction goal identify the input space X identify the output space Y identify the data D = (x 1, y 1 ),..., (x n, y n ) you d like to use what kinds of noise do you expect in the data? 25 / 33

38 Kinds of learning 26 / 33

39 Kinds of learning Supervised learning: given (x 1, y 1 ),..., (x n, y n ), learn f (x) = y 26 / 33

40 Kinds of learning Supervised learning: given (x 1, y 1 ),..., (x n, y n ), learn f (x) = y Unsupervised learning: given x 1,..., x n, learn patterns or structure 26 / 33

41 Kinds of learning Supervised learning: given (x 1, y 1 ),..., (x n, y n ), learn f (x) = y Unsupervised learning: given x 1,..., x n, learn patterns or structure Online learning: for i = 1,..., n, given x i, predict and observe y i, learn f (x) = y 26 / 33

42 Kinds of learning Supervised learning: given (x 1, y 1 ),..., (x n, y n ), learn f (x) = y Unsupervised learning: given x 1,..., x n, learn patterns or structure Online learning: for i = 1,..., n, given x i, predict and observe y i, learn f (x) = y Active learning: for i = 1,..., n, choose x i, predict and observe y i, learn f (x) = y 26 / 33

43 Kinds of learning Supervised learning: given (x 1, y 1 ),..., (x n, y n ), learn f (x) = y Unsupervised learning: given x 1,..., x n, learn patterns or structure Online learning: for i = 1,..., n, given x i, predict and observe y i, learn f (x) = y Active learning: for i = 1,..., n, choose x i, predict and observe y i, learn f (x) = y Reinforcement learning: for i = 1,..., n, choose x i, predict y i, observe reward r i, learn f (x) = y 26 / 33

44 Kinds of learning Supervised learning: given (x 1, y 1 ),..., (x n, y n ), learn f (x) = y Unsupervised learning: given x 1,..., x n, learn patterns or structure Online learning: for i = 1,..., n, given x i, predict and observe y i, learn f (x) = y Active learning: for i = 1,..., n, choose x i, predict and observe y i, learn f (x) = y Reinforcement learning: for i = 1,..., n, choose x i, predict y i, observe reward r i, learn f (x) = y this class: mostly supervised and unsupervised learning 26 / 33

45 Outline Stories Definitions Kinds of learning Syllabus Logistics 27 / 33

46 Course objectives (I) plot predict cluster impute denoise recommend understand 28 / 33

47 Course objectives (II) at the end of the course, you should have learned at least one method to solve any problem when not to trust your solution 29 / 33

48 Course objectives (II) at the end of the course, you should have learned at least one method to solve any problem when not to trust your solution the rest you can learn online / 33

49 Outline Stories Definitions Kinds of learning Syllabus Logistics 30 / 33

50 This class algorithms for big messy data learning to ask the right questions course website: (grading, course requirements, lectures, homework, etc) 31 / 33

51 Next steps ASAP: enroll (or drop) (or get on wait list) fill out course survey before next lecture: post a question or comment to piazza about this lecture due 8/29/17: homework 0... links on course website 32 / 33

52 Questions? 33 / 33