'Atomic events' diaporamas de présentation

Picturing program execution

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Training Intelligent Event Correlation Systems to Know Which Alerts Matter

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CS 440/ECE 448 Lecture 10: Probability

CS 440/ECE 448 Lecture 10: Probability. Slides by Svetlana Lazebnik, 9/2016 Modified by Mark Hasegawa-Johnson, 2/2019. Outline. Motivation: Why use probability? Laziness, Ignorance, and Randomness Rational Bettor Theorem Review of Key Concepts Outcomes, Events

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Organization

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Organization

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Monitoring XML Data on the Web

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Bayesian networks

Instructor: Vincent Conitzer. Bayesian networks. Rain and sprinklers example. sprinklers (Y). raining (X). P(X=1) = .3. P(Y=1) = .4. grass wet (Z). Each node has a conditional probability table (CPT). P(Z=1 | X=0, Y=0) = .1 P(Z=1 | X=0, Y=1) = .8 P(Z=1 | X=1, Y=0) = .7

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Event-Based Extractive Summarization

Event-Based Extractive Summarization. E. Filatova and V. Hatzivassiloglou Department of Computer Science Columbia University ( ACL 2004 ). Abstract.

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CPS 570: Artificial Intelligence Bayesian networks

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CPS 570: Artificial Intelligence Bayesian networks

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Instructor: Vincent Conitzer

Instructor: Vincent Conitzer. CPS 270: Artificial Intelligence http://www.cs.duke.edu/courses/fall08/cps270/ Introduction to probability. Uncertainty. So far in course, everything deterministic If I walk with my umbrella, I will not get wet But: there is some chance my umbrella will break!

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MIS 2000 Class 8 Operations and Information Systems

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CS 541: Artificial Intelligence

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