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Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, Oregon 97331 http://www.cs.orst.edu/~tgd Machine Learning:  Making Computer Science Scientific
Acknowledgements ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object]
Scenario 1: Reading Checks Find and read “courtesy amount” on checks:
Possible Methods: ,[object Object],[object Object]
Scenario 2:  VLSI Wafer Testing ,[object Object]
Which Chips (and how many) should be tested? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Possible Methods ,[object Object],[object Object],[object Object]
Scenario 3: Allocating mobile robot camera ,[object Object],[object Object]
Camera tradeoff ,[object Object],[object Object],[object Object],[object Object]
Possible Methods ,[object Object],[object Object]
Software Engineering Methodology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Challenges for SE Methodology ,[object Object],[object Object],[object Object]
(1) System requirements are hard to collect ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
(2) The system must resolve difficult tradeoffs ,[object Object],[object Object],[object Object],[object Object]
Machine Learning:  Replacing guesswork with data ,[object Object],[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object]
Basic Machine Learning Methods ,[object Object],[object Object],[object Object]
Supervised Learning Training Examples Learning Algorithm Classifier New Examples 8 8 3 6 0 1
AT&T/NCR Check Reading System Recognition transformer  is a neural network trained on 500,000 examples of characters The entire system is trained given entire checks as input and dollar amounts as output LeCun, Bottou, Bengio & Haffner (1998) Gradient-Based Learning Applied to Document Recognition
Check Reader Performance ,[object Object],[object Object],[object Object],[object Object]
Supervised Learning Summary ,[object Object],[object Object]
Density Estimation Training Examples Learning Algorithm Density Estimator P(chip i  is bad) = 0.42 Partially-tested wafer
On-Wafer Testing System ,[object Object],[object Object],W C209 C3 C2 C1 . . .
One-Step Value of Information ,[object Object],[object Object],[object Object]
On-Wafer Chip Test Results 3.8% increase in profit
Density Estimation Summary ,[object Object],[object Object]
Reinforcement Learning Environment state s reward r action a Agent’s goal: Choose actions to maximize total reward Action Selection Rule is called a “policy”:  a =   (s) agent
Reinforcement Learning Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Reinforcement Learning for Robot Navigation ,[object Object],[object Object],[object Object],[object Object]
Experimental Results: % trials robot reaches goal Busquets, Lopez de Mantaras, Sierra, Dietterich (2002)
Reinforcement Learning Summary ,[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object]
Fundamental Issues in Machine Learning ,[object Object],[object Object],[object Object],[object Object]
Incorporating Prior Knowledge ,[object Object],[object Object],[object Object],[object Object],[object Object]
Incorporating Learned Structures into Larger Systems ,[object Object],[object Object],[object Object],[object Object]
Making Reinforcement Learning Practical ,[object Object],[object Object]
The Triple Tradeoff ,[object Object],[object Object],[object Object],[object Object],[object Object]
Learning Algorithms ,[object Object],[object Object],[object Object],Training Data h Hypothesis Space
Triple Tradeoff ,[object Object],N = 1000 Hypothesis Space Complexity Accuracy N = 10 N = 100
Triple Tradeoff (2) Number of training examples N Accuracy Hypothesis Complexity H 1 H 2 H 3
Intuition ,[object Object],[object Object],[object Object]
Fixed versus Variable-Sized Hypothesis Spaces ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Corollary 1: Fixed H will underfit Number of training examples N Accuracy H 1 H 2 underfit
Corollary 2: Variable-sized H will overfit Hypothesis Space Complexity Accuracy N = 100 overfit
Ideal Learning Algorithm: Adapt complexity to data Hypothesis Space Complexity Accuracy N = 10 N = 100 N = 1000
Adapting Hypothesis Complexity to Data Complexity ,[object Object],[object Object],[object Object],[object Object],[object Object]
Corollary 3:  It is optimal to be suboptimal ,[object Object],[object Object],[object Object]
What’s going on? ,[object Object],[object Object],[object Object],[object Object]
Outline ,[object Object],[object Object],[object Object],[object Object]
The Data Explosion ,[object Object],[object Object],[object Object],[object Object],[object Object],http://spsosun.gsfc.nasa.gov/eosinfo/EOSDIS_Site/index.html
The Data Explosion (2) ,[object Object],[object Object],[object Object],[object Object]
The Data Explosion (3) http://www.cs.columbia.edu/~hgs/internet/traffic.html
Old Computer Science Conception of Data Store Retrieve
New Computer Science Conception of Data Store Build Models Solve Problems Problems Solutions
Machine Learning: Making Data Active ,[object Object],[object Object],[object Object],[object Object],[object Object]
Machine Learning and Computer Science ,[object Object],[object Object],[object Object],[object Object],[object Object]
Hardware Branch Prediction  Source: Jim é nez & Lin (2000)  Perceptron Learning for Predicting the Behavior of Conditional Branches
Instruction Scheduler for New CPU ,[object Object],[object Object],[object Object]
Instruction Scheduling ,[object Object],[object Object],[object Object],[object Object]
Computer Graphics: Video Textures ,[object Object],A B C D E F B D E D E F A Apply reinforcement learning to identify good transition points Arno Schödl, Richard Szeliski, David H. Salesin, Irfan Essa (SIGGRAPH 2000)
Video Textures Arno Schödl, Richard Szeliski, David H. Salesin, Irfan Essa (SIGGRAPH 2000) You can find this video at  Virtual Fish Tank Movie
Graphics: Image Analogies : :: : ? Hertzmann, Jacobs, Oliver, Curless, Salesin (2000) SIGGRAPH
Learning to Predict Textures Find p to minimize Euclidean distance between and B’(q) := A’(p) A(p) A’(p) B(q) B’(q)
Image Analogies : :: :
A video can be found at Image Analogies Movie
Summary ,[object Object],[object Object]
Machine Learning and Computer Science ,[object Object],[object Object],[object Object]
Computer Power and  Data Power ,[object Object],[object Object],[object Object]

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This is a heavily data-oriented

  • 1. Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, Oregon 97331 http://www.cs.orst.edu/~tgd Machine Learning: Making Computer Science Scientific
  • 2.
  • 3.
  • 4. Scenario 1: Reading Checks Find and read “courtesy amount” on checks:
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18.
  • 19. Supervised Learning Training Examples Learning Algorithm Classifier New Examples 8 8 3 6 0 1
  • 20. AT&T/NCR Check Reading System Recognition transformer is a neural network trained on 500,000 examples of characters The entire system is trained given entire checks as input and dollar amounts as output LeCun, Bottou, Bengio & Haffner (1998) Gradient-Based Learning Applied to Document Recognition
  • 21.
  • 22.
  • 23. Density Estimation Training Examples Learning Algorithm Density Estimator P(chip i is bad) = 0.42 Partially-tested wafer
  • 24.
  • 25.
  • 26. On-Wafer Chip Test Results 3.8% increase in profit
  • 27.
  • 28. Reinforcement Learning Environment state s reward r action a Agent’s goal: Choose actions to maximize total reward Action Selection Rule is called a “policy”: a =  (s) agent
  • 29.
  • 30.
  • 31. Experimental Results: % trials robot reaches goal Busquets, Lopez de Mantaras, Sierra, Dietterich (2002)
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41. Triple Tradeoff (2) Number of training examples N Accuracy Hypothesis Complexity H 1 H 2 H 3
  • 42.
  • 43.
  • 44. Corollary 1: Fixed H will underfit Number of training examples N Accuracy H 1 H 2 underfit
  • 45. Corollary 2: Variable-sized H will overfit Hypothesis Space Complexity Accuracy N = 100 overfit
  • 46. Ideal Learning Algorithm: Adapt complexity to data Hypothesis Space Complexity Accuracy N = 10 N = 100 N = 1000
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
  • 52.
  • 53. The Data Explosion (3) http://www.cs.columbia.edu/~hgs/internet/traffic.html
  • 54. Old Computer Science Conception of Data Store Retrieve
  • 55. New Computer Science Conception of Data Store Build Models Solve Problems Problems Solutions
  • 56.
  • 57.
  • 58. Hardware Branch Prediction Source: Jim é nez & Lin (2000) Perceptron Learning for Predicting the Behavior of Conditional Branches
  • 59.
  • 60.
  • 61.
  • 62. Video Textures Arno Schödl, Richard Szeliski, David H. Salesin, Irfan Essa (SIGGRAPH 2000) You can find this video at Virtual Fish Tank Movie
  • 63. Graphics: Image Analogies : :: : ? Hertzmann, Jacobs, Oliver, Curless, Salesin (2000) SIGGRAPH
  • 64. Learning to Predict Textures Find p to minimize Euclidean distance between and B’(q) := A’(p) A(p) A’(p) B(q) B’(q)
  • 66. A video can be found at Image Analogies Movie
  • 67.
  • 68.
  • 69.