1. Machine learning, probabilistic modelling
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Machine learning, probabilistic modelling
Stuart Russell
Computer Science Division, UC Berkeley

2. Outline Some basic aspects of machine learning
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Outline
Some basic aspects of machine learning
Example: detecting artifacts in ICU data
Example: probabilistic data association
Multitarget tracking
Freeway traffic
CiteSeer
Sibyl attacks on recommender systems

3. Machine learning: model-free
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Machine learning: model-free
Learning
hypothesis
data

4. Model-free learning contd.
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Model-free learning contd.
Supervised learning
Input: x1, f(x1) … xn, f(xn)
(many possible input and label spaces)
Output: h  f
E.g., f classifies xi as earthquake/explosion
Unsupervised learning
Input: x1, … xn
Output: clustering of inputs into categories

5. Model-free learning contd.
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Model-free learning contd.
Application, form of data influence choice of hypothesis class for H
Linear models, logistic regression
Decision trees (classification or regression)
Nonparametric (instance-based)
Kernel methods
effectively linear separators in a transformed high-dimensional input space
Probabilistic grammars for strings
Etc.

6. Model-based learning Learning prior knowledge knowledge data
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Model-based learning
prior
knowledge
Learning
knowledge
data

7. Model-based learning Learning prior knowledge knowledge data
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Model-based learning
prior
knowledge
Learning
knowledge
data

8. Bayesian model-based learning
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Bayesian model-based learning
Generative approach
P(world) describes prior over what is (source), also over model parameters, structure
P(signal | world) describes sensor model (channel)
Given new signal, compute P(world | signal)
Learning
Posterior over parameters (or structure) given data
Or use maximum a posteriori, maximum likelihood
Substantial advances modeling capabilities, general-purpose inference algorithms
Applications with millions of parameters, gigabytes of data are fairly routine

9. 1/1/2019 7:20:22 AM
This is what an ICU looks like: ventilator, fluids, monitors;
~200 medical procedures per day, many potentially fatal

10. Artifact events ubiquitous
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11. Blood pressure signals
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Blood pressure signals

12. 1/1/2019 7:20:22 AM
Artifact events
Goal: detect, categorize, and correct for artifacts in blood pressure signal

13. 1/1/2019 7:20:22 AM
Generative model
Parameters for event duration, frequency trained on small sample of one-second data
Detection uses equivalent one-minute model based on measurement and artifact processes

14. 1/1/2019 7:20:22 AM
ALARM
2 days, 19 false alarms, 7 true alarms, 1 false+true; all correct 14

15. Example: classical data association

16. Example: classical data association

17. Example: classical data association

18. Example: classical data association

19. Example: classical data association

20. Example: classical data association

21. Generative model World = aircraft, trajectories, blip associations
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Generative model
World = aircraft, trajectories, blip associations
#Aircraft ~ NumAircraftPrior();
State(a, t) if t = 0 then ~ InitState() else ~ StateTransition(State(a, t-1));
#Blip(Source = a, Time = t) ~ NumDetectionsCPD(State(a, t));
#Blip(Time = t) ~ NumFalseAlarmsPrior();
ApparentPos(r) if (Source(r) = null) then ~ FalseAlarmDistrib() else ~ ObsCPD(State(Source(r), Time(r)));

22. Aircraft Tracking Results
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Aircraft Tracking Results
[Oh et al., CDC 2004]
(simulated data)
MCMC has smallest error, hardly degrades at all as tracks get dense
MCMC is nearly as fast as greedy algorithm; much faster than MHT
[Figures by Songhwai Oh]

23. Extending the Model: Air Bases
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Extending the Model: Air Bases
#Aircraft(InitialBase = b) ~ InitialAircraftPerBasePrior();
CurBase(a, t) if t = 0 then = InitialBase(b) elseif TakesOff(a, t-1) then = null elseif Lands(a, t-1) then = Dest(a, t-1) else = CurBase(a, t-1);
InFlight(a, t) = (CurBase(a, t) = null);
TakesOff(a, t) if !InFlight(a, t) then ~ Bernoulli(0.1);
Lands(a, t) if InFlight(a, t) then ~ LandingCPD(State(a, t), Location(Dest(a, t)));
Dest(a, t) if TakesOff(a, t) then ~ Uniform({Base b}) elseif InFlight(a, t) then = Dest(a, t-1)
State(a, t) if TakesOff(a, t-1) then ~ InitState(Location(CurBase(a, t-1))) elseif InFlight(a, t) then ~ StateTrans(State(a, t-1), Location(Dest(a, t)));

24. Unknown Air Bases Just add two more lines: #AirBase ~ NumBasesPrior();
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Unknown Air Bases
Just add two more lines:
#AirBase ~ NumBasesPrior();
Location(b) ~ BaseLocPrior();

25. Example: traffic surveillance
Multiple distributed sensors
Uncertain, time-varying travel time
Prediction error >>> object separation

26. Example: Citation Matching
[Lashkari et al 94] Collaborative Interface Agents, Yezdi Lashkari, Max Metral, and Pattie Maes, Proceedings of the Twelfth National Conference on Articial Intelligence, MIT Press, Cambridge, MA, 1994.
Metral M. Lashkari, Y. and P. Maes. Collaborative interface agents. In Conference of the American Association for Artificial Intelligence, Seattle, WA, August 1994.
Are these descriptions of the same object?
Core task in CiteSeer, Google Scholar

27. (Simplified) BLOG model
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(Simplified) BLOG model
#Researcher ~ NumResearchersPrior();
Name(r) ~ NamePrior();
#Paper(FirstAuthor = r) ~
NumPapersPrior(Position(r));
Title(p) ~ TitlePrior();
PubCited(c) ~ Uniform({Paper p});
Text(c) ~ NoisyCitationGrammar (Name(FirstAuthor(PubCited(c))),
Title(PubCited(c)));

28. Citation Matching Results
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Citation Matching Results
Four data sets of ~ citations, referring to ~ papers

29. Example: Sibyl attacks
Typically between 100 and 10,000 real entities
About 90% are honest, have one identity
Dishonest entities own between 10 and 1000 identities.
Transactions may occur between identities
If two identities are owned by the same entity (sibyls), then a transaction is highly likely;
Otherwise, transaction is less likely (depending on honesty of each identity’s owner).
An identity may recommend another after a transaction:
Sibyls with the same owner usually recommend each other;
Otherwise, probability of recommendation depends on the honesty of the two entities.

30. 1/1/2019 7:20:22 AM
#Entity ~ LogNormal[6.9, 2.3]();
Honest(x) ~ Boolean[0.9]();
#Identity(Owner = x) ~
if Honest(x) then 1 else LogNormal[4.6,2.3]();
Transaction(x,y) ~
if Owner(x) = Owner(y) then SibylPrior ()
else TransactionPrior(Honest(Owner(x)),
Honest(Owner(y)));
Recommends(x,y) ~
if Transaction(x,y) then
if Owner(x) = Owner(y) then Boolean[0.99]()
else RecPrior(Honest(Owner(x)),
Evidence: lots of transactions and recommendations, maybe some Honest(.) assertions
Query: Honest(x)

31. Summary Generative approach to machine learning Can accommodate
strong prior knowledge
heterogeneous data
noise, artifacts
Vertically integrated probability models (not pipeline) connect events, transmission, detection, association