1. CS194-10 Fall 2011 Introduction to Machine Learning Machine Learning: An Overview

2. CS 194-10 Fall 2011, Stuart Russell
People
Avital Steinitz
2nd year CS
PhD student
Stuart Russell
30th-year CS
PhD student
Mert Pilanci
2nd year EE
PhD student
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

3. Administrative details
Web page
Newsgroup
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

4. CS 194-10 Fall 2011, Stuart Russell
Course outline
Overview of machine learning (today)
Classical supervised learning
Linear regression, perceptrons, neural nets, SVMs, decision trees, nearest neighbors, and all that
A little bit of theory, a lot of applications
Learning probabilistic models
Probabilistic classifiers (logistic regression, etc.)
Unsupervised learning, density estimation, EM
Bayes net learning
Time series models
Dimensionality reduction
Gaussian process models
Language models
Bandits and other exciting topics
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

5. CS 194-10 Fall 2011, Stuart Russell
Lecture outline
Goal: Provide a framework for understanding all the detailed content to come, and why it matters
Learning: why and how
Supervised learning
Classical: finding simple, accurate hypotheses
Probabilistic: finding likely hypotheses
Bayesian: updating belief in hypotheses
Data and applications
Expressiveness and cumulative learning
CTBT
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

6. CS 194-10 Fall 2011, Stuart Russell
Learning is….
… a computational process for improving performance based on experience
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CS Fall 2011, Stuart Russell

7. CS 194-10 Fall 2011, Stuart Russell
Learning: Why?
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CS Fall 2011, Stuart Russell

8. CS 194-10 Fall 2011, Stuart Russell
Learning: Why?
The baby, assailed by eyes, ears, nose, skin, and entrails at once, feels it all as one great blooming, buzzing confusion …
[William James, 1890]
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

9. CS 194-10 Fall 2011, Stuart Russell
Learning: Why?
The baby, assailed by eyes, ears, nose, skin, and entrails at once, feels it all as one great blooming, buzzing confusion …
[William James, 1890]
Learning is essential for unknown environments,
i.e., when the designer lacks omniscience
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

10. CS 194-10 Fall 2011, Stuart Russell
Learning: Why?
Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child brain is something like a notebook as one buys it from the stationer's. Rather little mechanism, and lots of blank sheets.
[Alan Turing, 1950]
Learning is useful as a system construction method, i.e., expose the system to reality rather than trying to write it down
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CS Fall 2011, Stuart Russell

11. CS 194-10 Fall 2011, Stuart Russell
Learning: How?
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CS Fall 2011, Stuart Russell

12. CS 194-10 Fall 2011, Stuart Russell
Learning: How?
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CS Fall 2011, Stuart Russell

13. CS 194-10 Fall 2011, Stuart Russell
Learning: How?
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CS Fall 2011, Stuart Russell

14. CS 194-10 Fall 2011, Stuart Russell
Learning: How?
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CS Fall 2011, Stuart Russell

15. Structure of a learning agent
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CS Fall 2011, Stuart Russell

16. Design of learning element
Key questions:
What is the agent design that will implement the desired performance?
Improve the performance of what piece of the agent system and how is that piece represented?
What data are available relevant to that piece? (In particular, do we know the right answers?)
What knowledge is already available?
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

17. CS 194-10 Fall 2011, Stuart Russell
Examples
Agent design
Component
Representation
Feedback
Knowledge
Alpha-beta search
Evaluation function
Linear polynomial
Win/loss
Rules of game;
Coefficient signs
Logical planning agent
Transition model
(observable envt)
Successor-state axioms
Action outcomes
Available actions;
Argument types
Utility-based patient monitor
Physiology/sensor model
Dynamic Bayesian network
Observation sequences
Gen physiology;
Sensor design
Satellite image pixel classifier
Classifier (policy)
Markov random field
Partial labels
Coastline;
Continuity scales
Supervised learning: correct answers for each training instance
Reinforcement learning: reward sequence, no correct answers
Unsupervised learning: “just make sense of the data”
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

18. CS 194-10 Fall 2011, Stuart Russell
Supervised learning
3/11/2017 3:36:17 PM
To learn an unknown target function f
Input: a training set of labeled examples (xj,yj) where yj = f(xj)
E.g., xj is an image, f(xj) is the label “giraffe”
E.g., xj is a seismic signal, f(xj) is the label “explosion”
Output: hypothesis h that is “close” to f, i.e., predicts well on unseen examples (“test set”)
Many possible hypothesis families for h
Linear models, logistic regression, neural networks, decision trees, examples (nearest-neighbor), grammars, kernelized separators, etc etc
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

19. CS 194-10 Fall 2011, Stuart Russell
Supervised learning
3/11/2017 3:36:17 PM
To learn an unknown target function f
Input: a training set of labeled examples (xj,yj) where yj = f(xj)
E.g., xj is an image, f(xj) is the label “giraffe”
E.g., xj is a seismic signal, f(xj) is the label “explosion”
Output: hypothesis h that is “close” to f, i.e., predicts well on unseen examples (“test set”)
Many possible hypothesis families for h
Linear models, logistic regression, neural networks, decision trees, examples (nearest-neighbor), grammars, kernelized separators, etc etc
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

20. CS 194-10 Fall 2011, Stuart Russell
Supervised learning
3/11/2017 3:36:17 PM
To learn an unknown target function f
Input: a training set of labeled examples (xj,yj) where yj = f(xj)
E.g., xj is an image, f(xj) is the label “giraffe”
E.g., xj is a seismic signal, f(xj) is the label “explosion”
Output: hypothesis h that is “close” to f, i.e., predicts well on unseen examples (“test set”)
Many possible hypothesis families for h
Linear models, logistic regression, neural networks, decision trees, examples (nearest-neighbor), grammars, kernelized separators, etc etc
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

21. Example: object recognition
3/11/2017 3:36:17 PM x
f(x)
giraffe
giraffe
giraffe
llama
llama
llama
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CS Fall 2011, Stuart Russell

22. Example: object recognition
3/11/2017 3:36:17 PM x
f(x)
giraffe
giraffe
giraffe
llama
llama
llama X=
f(x)=?
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CS Fall 2011, Stuart Russell

23. Example: curve fitting
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24. Example: curve fitting
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25. Example: curve fitting
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26. Example: curve fitting
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27. Example: curve fitting
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28. CS 194-10 Fall 2011, Stuart Russell
Basic questions
Which hypothesis space H to choose?
How to measure degree of fit?
How to trade off degree of fit vs. complexity?
“Ockham’s razor”
How do we find a good h?
How do we know if a good h will predict well?
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CS Fall 2011, Stuart Russell

29. Philosophy of Science (Physics)
Which hypothesis space H to choose?
Deterministic hypotheses, usually mathematical formulas and/or logical sentences; implicit relevance determination
How to measure degree of fit?
Ideally, h will be consistent with data
How to trade off degree of fit vs. complexity?
Theory must be correct up to “experimental error”
How do we find a good h?
Intuition, imagination, inspiration (invent new terms!!)
How do we know if a good h will predict well?
Hume’s Problem of Induction: most philosophers give up
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CS Fall 2011, Stuart Russell

30. Kolmogorov complexity (also MDL, MML)
Which hypothesis space H to choose?
All Turing machines (or programs for a UTM)
How to measure degree of fit?
Fit is perfect (program has to output data exactly)
How to trade off degree of fit vs. complexity?
Minimize size of program
How do we find a good h?
Undecidable (unless we bound time complexity of h)
How do we know if a good h will predict well?
(recent theory borrowed from PAC learning)
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

31. Classical stats/ML: Minimize loss function
Which hypothesis space H to choose?
E.g., linear combinations of features: hw(x) = wTx
How to measure degree of fit?
Loss function, e.g., squared error Σj (yj – wTx)2
How to trade off degree of fit vs. complexity?
Regularization: complexity penalty, e.g., ||w||2
How do we find a good h?
Optimization (closed-form, numerical); discrete search
How do we know if a good h will predict well?
Try it and see (cross-validation, bootstrap, etc.)
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

32. Probabilistic: Max. likelihood, max. a priori
Which hypothesis space H to choose?
Probability model P(y | x,h) , e.g., Y ~ N(wTx,σ2)
How to measure degree of fit?
Data likelihood Πj P(yj | xj,h)
How to trade off degree of fit vs. complexity?
Regularization or prior: argmaxh P(h) Πj P(yj | xj,h) (MAP)
How do we find a good h?
Optimization (closed-form, numerical); discrete search
How do we know if a good h will predict well?
Empirical process theory (generalizes Chebyshev, CLT, PAC…);
Key assumption is (i)id
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

33. Bayesian: Computing posterior over H
Which hypothesis space H to choose?
All hypotheses with nonzero a priori probability
How to measure degree of fit?
Data probability, as for MLE/MAP
How to trade off degree of fit vs. complexity?
Use prior, as for MAP
How do we find a good h?
Don’t! Bayes predictor P(y|x,D) = Σh P(y|x,h) P(D|h) P(h)
How do we know if a good h will predict well?
Silly question! Bayesian prediction is optimal!!
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

34. Bayesian: Computing posterior over H
Which hypothesis space H to choose?
All hypotheses with nonzero a priori probability
How to measure degree of fit?
Data probability, as for MLE/MAP
How to trade off degree of fit vs. complexity?
Use prior, as for MAP
How do we find a good h?
Don’t! Bayes predictor P(y|x,D) = Σh P(y|x,h) P(D|h) P(h)
How do we know if a good h will predict well?
Silly question! Bayesian prediction is optimal!!
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

35. Neon sculpture at Autonomy Corp.
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36. CS 194-10 Fall 2011, Stuart Russell
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37. CS 194-10 Fall 2011, Stuart Russell
Lots of data
Web: estimated Google index 45 billion pages
Clickstream data: TB/day
Transaction data: 5-50 TB/day
Satellite image feeds: ~1TB/day/satellite
Sensor networks/arrays
CERN Large Hadron Collider ~100 petabytes/day
Biological data: 1-10TB/day/sequencer
TV: 2TB/day/channel; YouTube 4TB/day uploaded
Digitized telephony: ~100 petabytes/day
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CS Fall 2011, Stuart Russell

38. CS 194-10 Fall 2011, Stuart Russell
This is what an ICU looks like: ventilator, fluids, monitors;
~200 medical procedures per day, many potentially fatal
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CS Fall 2011, Stuart Russell

39. CS 194-10 Fall 2011, Stuart Russell
Real data are messy
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40. Arterial blood pressure (high/low/mean) 1s
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41. Application: satellite image analysis
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CS Fall 2011, Stuart Russell

42. Application: Discovering DNA motifs
...TTGGAACAACCATGCACGGTTGATTCGTGCCTGTGACCGCGCGCCTCACACGGAAGACGCAGCCACCGGTTGTGATG TCATAGGGAATTCCCCATGTCGTGAATAATGCCTCGAATGATGAGTAATAGTAAAACGCAGGGGAGGTTCTTCAGTAGTA TCAATATGAGACACATACAAACGGGCGTACCTACCGCAGCTCAAAGCTGGGTGCATTTTTGCCAAGTGCCTTACTGTTAT CTTAGGACGGAAATCCACTATAAGATTATAGAAAGGAAGGCGGGCCGAGCGAATCGATTCAATTAAGTTATGTCACAAGG GTGCTATAGCCTATTCCTAAGATTTGTACGTGCGTATGACTGGAATTAATAACCCCTCCCTGCACTGACCTTGACTGAAT AACTGTGATACGACGCAAACTGAACGCTGCGGGTCCTTTATGACCACGGATCACGACCGCTTAAGACCTGAGTTGGAGTT GATACATCCGGCAGGCAGCCAAATCTTTTGTAGTTGAGACGGATTGCTAAGTGTGTTAACTAAGACTGGTATTTCCACTA GGACCACGCTTACATCAGGTCCCAAGTGGACAACGAGTCCGTAGTATTGTCCACGAGAGGTCTCCTGATTACATCTTGAA GTTTGCGACGTGTTATGCGGATGAAACAGGCGGTTCTCATACGGTGGGGCTGGTAAACGAGTTCCGGTCGCGGAGATAAC TGTTGTGATTGGCACTGAAGTGCGAGGTCTTAAACAGGCCGGGTGTACTAACCCAAAGACCGGCCCAGCGTCAGTGA...
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

43. Application: Discovering DNA motifs
...TTGGAACAACCATGCACGGTTGATTCGTGCCTGTGACCGCGCGCCTCACACGGAAGACGCAGCCACCGGTTGTGATG TCATAGGGAATTCCCCATGTCGTGAATAATGCCTCGAATGATGAGTAATAGTAAAACGCAGGGGAGGTTCTTCAGTAGTA TCAATATGAGACACATACAAACGGGCGTACCTACCGCAGCTCAAAGCTGGGTGCATTTTTGCCAAGTGCCTTACTGTTAT CTTAGGACGGAAATCCACTATAAGATTATAGAAAGGAAGGCGGGCCGAGCGAATCGATTCAATTAAGTTATGTCACAAGG GTGCTATAGCCTATTCCTAAGATTTGTACGTGCGTATGACTGGAATTAATAACCCCTCCCTGCACTGACCTTGACTGAAT AACTGTGATACGACGCAAACTGAACGCTGCGGGTCCTTTATGACCACGGATCACGACCGCTTAAGACCTGAGTTGGAGTT GATACATCCGGCAGGCAGCCAAATCTTTTGTAGTTGAGACGGATTGCTAAGTGTGTTAACTAAGACTGGTATTTCCACTA GGACCACGCTTACATCAGGTCCCAAGTGGACAACGAGTCCGTAGTATTGTCCACGAGAGGTCTCCTGATTACATCTTGAA GTTTGCGACGTGTTATGCGGATGAAACAGGCGGTTCTCATACGGTGGGGCTGGTAAACGAGTTCCGGTCGCGGAGATAAC TGTTGTGATTGGCACTGAAGTGCGAGGTCTTAAACAGGCCGGGTGTACTAACCCAAAGACCGGCCCAGCGTCAGTGA...
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CS Fall 2011, Stuart Russell

44. CS 194-10 Fall 2011, Stuart Russell
Application: User website behavior from clickstream data (from P. Smyth, UCI)
, -, 3/22/00, 10:35:11, W3SVC, SRVR1, , 781, 363, 875, 200, 0, GET, /top.html, -,
, -, 3/22/00, 10:35:16, W3SVC, SRVR1, , 5288, 524, 414, 200, 0, POST, /spt/main.html, -,
, -, 3/22/00, 10:35:17, W3SVC, SRVR1, , 30, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
, -, 3/22/00, 16:18:50, W3SVC, SRVR1, , 60, 425, 72, 304, 0, GET, /top.html, -,
, -, 3/22/00, 16:18:58, W3SVC, SRVR1, , 8322, 527, 414, 200, 0, POST, /spt/main.html, -,
, -, 3/22/00, 16:18:59, W3SVC, SRVR1, , 0, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
, -, 3/22/00, 20:54:37, W3SVC, SRVR1, , 140, 199, 875, 200, 0, GET, /top.html, -,
, -, 3/22/00, 20:54:55, W3SVC, SRVR1, , 17766, 365, 414, 200, 0, POST, /spt/main.html, -,
, -, 3/22/00, 20:54:55, W3SVC, SRVR1, , 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
, -, 3/22/00, 20:55:07, W3SVC, SRVR1, , 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
, -, 3/22/00, 20:55:36, W3SVC, SRVR1, , 1061, 382, 414, 200, 0, POST, /spt/main.html, -,
, -, 3/22/00, 20:55:36, W3SVC, SRVR1, , 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
, -, 3/22/00, 20:55:39, W3SVC, SRVR1, , 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
, -, 3/22/00, 20:56:03, W3SVC, SRVR1, , 1081, 382, 414, 200, 0, POST, /spt/main.html, -,
, -, 3/22/00, 20:56:04, W3SVC, SRVR1, , 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,
, -, 3/22/00, 20:56:33, W3SVC, SRVR1, , 0, 262, 72, 304, 0, GET, /top.html, -,
, -, 3/22/00, 20:56:52, W3SVC, SRVR1, , 19598, 382, 414, 200, 0, POST, /spt/main.html, -,
User 1 2 3 2 2 3 3 3 1 1 1 3 1 3 3 3 3
User 2 3 3 3 1 1 1
User 3 7 7 7 7 7 7 7 7
User 4 1 5 1 1 1 5 1 5 1 1 1 1 1 1
User 5 5 1 1 5 … …
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

45. Application: social network analysis
HP Labs data
500 users, 20k connections
evolving over time
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CS Fall 2011, Stuart Russell

46. Application: spam filtering
200 billion spam messages sent per day
Asymmetric cost of false positive/false negative
Weak label: discarded without reading
Strong label (“this is spam”) hard to come by
Standard iid assumption violated: spammers alter spam generators to evade or subvert spam filters (“adversarial learning” task)
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CS Fall 2011, Stuart Russell

47. CS 194-10 Fall 2011, Stuart Russell
Learning
3/11/2017 3:36:18 PM
Learning
knowledge
data
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CS Fall 2011, Stuart Russell

48. CS 194-10 Fall 2011, Stuart Russell
Learning
3/11/2017 3:36:18 PM
prior
knowledge
Learning
knowledge
data
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CS Fall 2011, Stuart Russell

49. CS 194-10 Fall 2011, Stuart Russell
Learning
3/11/2017 3:36:18 PM
prior
knowledge
Learning
knowledge
data
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CS Fall 2011, Stuart Russell

50. CS 194-10 Fall 2011, Stuart Russell
Learning
3/11/2017 3:36:18 PM
prior
knowledge
Learning
knowledge
data
Crucial open problem: weak intermediate forms of knowledge that support future generalizations
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51. CS 194-10 Fall 2011, Stuart Russell
Example
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52. CS 194-10 Fall 2011, Stuart Russell
Example
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53. CS 194-10 Fall 2011, Stuart Russell
Example
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54. Example – arriving at Sao Paulo, Brazil
Bem-vindo!
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55. Example – arriving at Sao Paulo, Brazil
Bem-vindo!
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CS Fall 2011, Stuart Russell

56. Example – arriving at Sao Paulo, Brazil
Bem-vindo!
Bem-vindo!
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57. Example – arriving at Sao Paulo, Brazil
Bem-vindo!
Bem-vindo!
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58. CS 194-10 Fall 2011, Stuart Russell
Weak prior knowledge
In this case, people in a given country (and city) tend to speak the same language
Where did this knowledge come from?
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59. CS 194-10 Fall 2011, Stuart Russell
Weak prior knowledge
In this case, people in a given country (and city) tend to speak the same language
Where did this knowledge come from?
Experience with other countries
“Common sense” – i.e., knowledge of how societies and languages work
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CS Fall 2011, Stuart Russell

60. CS 194-10 Fall 2011, Stuart Russell
Weak prior knowledge
In this case, people in a given country (and city) tend to speak the same language
Where did this knowledge come from?
Experience with other countries
“Common sense” – i.e., knowledge of how societies and languages work
And where did that knowledge come from?
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

61. Knowledge? What is knowledge? All I know is samples!! [V. Vapnik]
All knowledge derives, directly or indirectly, from experience of individuals
Knowledge serves as a directly applicable shorthand for all that experience – better than requiring constant review of the entire sensory/evolutionary history of the human race
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62. CS 194-10 Fall 2011, Stuart Russell
Expressiveness
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63. The world has things in it!!
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Expressive language => concise models
=> fast learning, sometimes fast reasoning
E.g., rules of chess:
1 page in first-order logic
On(color,piece,x,y,t)
~ pages in propositional logic
WhiteKingOnC4Move12
~ pages as atomic-state model
R.B.KB.RPPP..PPP..N..N…..PP….q.pp..Q..n..n..ppp..pppr.b.kb.r
[Note: chess is a tiny problem compared to the real world]
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64. Brief history of expressiveness
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probability
logic
atomic
propositional
first-order/relational
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65. Brief history of expressiveness
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probability
5th C B.C.
logic
atomic
propositional
first-order/relational
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66. Brief history of expressiveness
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17th C
probability
5th C B.C.
logic
atomic
propositional
first-order/relational
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67. Brief history of expressiveness
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17th C
probability
5th C B.C.
19th C
logic
atomic
propositional
first-order/relational
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68. Brief history of expressiveness
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17th C
20th C
probability
5th C B.C.
19th C
logic
atomic
propositional
first-order/relational
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69. Brief history of expressiveness
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17th C
20th C
21st C
probability
5th C B.C.
19th C
logic
atomic
propositional
first-order/relational
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70. Brief history of expressiveness
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Bernoulli
Categorical
Uni. Gaussian
(H)MMs
Bayes nets
MRFs
Multi. Gaussians
DBNs
Kalman filters
RPMs
BLOG
MLNs
(DBLOG)
probability
First-order logic
Database systems
Programs
First-order STRIPS
Temporal logic
OBDDs, k-CNF
Decision trees
Perceptrons
Propositional STRIPS
Register circuits
logic
Finite automata
atomic
propositional
first-order/relational
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CS Fall 2011, Stuart Russell

71. CTBT: Comprehensive Nuclear-Test-Ban Treaty
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Bans testing of nuclear weapons on earth
Allows for outside inspection of 1000km2
182/195 states have signed
153/195 have ratified
Need 9 more ratifications including US, China
US Senate refused to ratify in 1998
“too hard to monitor”
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72. CS 194-10 Fall 2011, Stuart Russell
2053 nuclear explosions
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74. CS 194-10 Fall 2011, Stuart Russell
254 monitoring stations
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76. CS 194-10 Fall 2011, Stuart Russell
The problem
Given waveform traces from all seismic stations, figure out what events occurred when and where
Traces at each sensor station may be preprocessed to form “detections” (90% are not real)
ARID ORID STA PH BEL DELTA SEAZ ESAZ TIME TDEF AZRES ADEF SLORES SDEF WGT VMODEL LDDATE
WRA P d d d IASP :54:27
FITZ P d d n IASP :54:27
MKAR P d d d IASP :54:27
ASAR P d d d IASP :54:27
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77. CS 194-10 Fall 2011, Stuart Russell
What do we know?
Events happen randomly; each has a time, location, depth, magnitude; seismicity varies with location
Seismic waves of many kinds (“phases”) travel through the Earth
Travel time and attenuation depend on phase and source/destination
Arriving waves may or may not be detected, depending on sensor and local noise environment
Local noise may also produce false detections
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82. CS 194-10 Fall 2011, Stuart Russell
3/11/2017 3:36:18 PM
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

83. CS 194-10 Fall 2011, Stuart Russell
3/11/2017 3:36:18 PM
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

84. CS 194-10 Fall 2011, Stuart Russell
3/11/2017 3:36:18 PM
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

85. CS 194-10 Fall 2011, Stuart Russell
3/11/2017 3:36:18 PM
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

86. CS 194-10 Fall 2011, Stuart Russell
3/11/2017 3:36:18 PM
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

87. CS 194-10 Fall 2011, Stuart Russell
3/11/2017 3:36:18 PM
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

88. CS 194-10 Fall 2011, Stuart Russell
3/11/2017 3:36:18 PM
# SeismicEvents ~ Poisson[TIME_DURATION*EVENT_RATE];
IsEarthQuake(e) ~ Bernoulli(.999);
EventLocation(e) ~ If IsEarthQuake(e) then EarthQuakeDistribution()
Else UniformEarthDistribution();
Magnitude(e) ~ Exponential(log(10)) + MIN_MAG;
Distance(e,s) = GeographicalDistance(EventLocation(e), SiteLocation(s));
IsDetected(e,p,s) ~ Logistic[SITE_COEFFS(s,p)](Magnitude(e), Distance(e,s);
#Arrivals(site = s) ~ Poisson[TIME_DURATION*FALSE_RATE(s)];
#Arrivals(event=e, site) = If IsDetected(e,s) then 1 else 0;
Time(a) ~ If (event(a) = null) then Uniform(0,TIME_DURATION)
else IASPEI(EventLocation(event(a)),SiteLocation(site(a)),Phase(a)) + TimeRes(a);
TimeRes(a) ~ Laplace(TIMLOC(site(a)), TIMSCALE(site(a)));
Azimuth(a) ~ If (event(a) = null) then Uniform(0, 360)
else GeoAzimuth(EventLocation(event(a)),SiteLocation(site(a)) + AzRes(a);
AzRes(a) ~ Laplace(0, AZSCALE(site(a)));
Slow(a) ~ If (event(a) = null) then Uniform(0,20)
else IASPEI-SLOW(EventLocation(event(a)),SiteLocation(site(a)) + SlowRes(site(a));
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

89. Learning with prior knowledge
Instead of learning a mapping from detection histories to event bulletins, learn local pieces of an overall structured model:
Event location prior (A6)
Predictive travel time model (A1)
Phase type classifier (A2)
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

90. Event location prior (A6)
3/11/2017 3:36:18 PM
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

91. Travel time prediction (A1)
How long does it take for a seismic signal to get from A to B? This is the travel time T(A,B)
If we know this accurately, and we know the arrival times t1, t2, t3, … at several stations B1, B2, B3, …, we can find an accurate estimate of the location A and time t for the event, such that
T(A,Bi) ≈ ti – t for all i
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

92. CS 194-10 Fall 2011, Stuart Russell
Earth 101
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

93. Seismic “phases” (wave types/paths)
Seismic energy is emitted in different types of waves; there are also qualitatively distinct paths (e.g., direct vs reflected from surface vs. refracted through core). P and S are the direct waves; P is faster
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

94. CS 194-10 Fall 2011, Stuart Russell
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

95. IASP 91 reference velocity model
Spherically symmetric, Vphase(depth); from this, obtain Tpredicted(A,B).
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

96. IASP91 inaccuracy is too big!
Earth is inhomogeneous: variations in crust thickness and rock properties (“fast” and “slow”)
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell

97. Travel time residuals (Tactual – Tpredicted)
Residual surface (wrt a particular station) is locally smooth; estimate by local regression
Lecture 1 8/25/11
CS Fall 2011, Stuart Russell