'Conditional probability distribution' diaporamas de présentation

Parameter Learning

Parameter Learning. Announcements. Midterm 24 th 7-9pm, NVIDIA. Midterm review in class next Tuesday. Extra study material for midterm (after class). Homework back. Regrade process. Looking into reflex agent on pacman. Some changes to the schedule.

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Generative Models

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Introduction to Machine Learning

Introduction to Machine Learning. Learning. Agent has made observations ( data ) Now must make sense of it ( hypotheses ) Hypotheses alone may be important (e.g., in basic science) For inference (e.g., forecasting) To take sensible actions (decision making)

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More probability

More probability. CS151 David Kauchak Fall 2010. Some material borrowed from : Sara Owsley Sood and others. Admin. Assignment 4 out Work in partners Part 1 due by Thursday at the beginning of class Midterm exam time Review next Tuesday. Joint distributions.

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Classification and clustering methods development and implementation for unstructured documents collections

Classification and clustering methods development and implementation for unstructured documents collections. by Osipova Nataly St.Petesburg State University Faculty of Applied Mathematics and Control Processes Department of Programming Technology. Contents. Introduction

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Conditional Random Fields model

Conditional Random Fields model. QingSong.Guo. Recent work. XML keyword query refinement Two ways: Focus on XML tree structure Focus on keywords. XML tree. In keyword query, there are many nodes in the XML tree matching the keywords.

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Bayesian Networks: A Tutorial

Bayesian Networks: A Tutorial. Yeni Herdiyeni. Introduction. Suppose you are trying to determine if a patient has inhalational anthrax. You observe the following symptoms: The patient has a cough The patient has a fever The patient has difficulty breathing. Introduction.

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Reasoning Under Uncertainty: Independence and Inference

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Nicholas Zabaras, Veera Sundararaghavan and Sethuraman Sankaran

An information-theoretic approach for obtaining property PDFs from macro specifications of microstructural variability. Nicholas Zabaras, Veera Sundararaghavan and Sethuraman Sankaran. Materials Process Design and Control Laboratory

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Reasoning Under Uncertainty: Bayesian networks intro

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Nonlinear Model-Based Estimation Algorithms: Tutorial and Recent Developments

Nonlinear Model-Based Estimation Algorithms: Tutorial and Recent Developments. Mark L. Psiaki Sibley School of Mechanical & Aerospace Engr., Cornell University. Acknowledgements. Collaborators Paul Kintner , former Cornell ECE faculty member Steve Powell , Cornell ECE research engineer

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Artificial Intelligence

Artificial Intelligence. Universitatea Politehnica Bucuresti Anul universitar 2003-2004 Adina Magda Florea http://www.cs.pub.ro/~ia. Lecture no. 7. Uncertain knowledge and reasoning Probability theory Bayesian networks Certainty factors. 2. 1. Probability theory.

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Texture Synthesis by Non-parametric Sampling

Texture Synthesis by Non-parametric Sampling. Alexei Efros and Thomas Leung UC Berkeley. Goal of Texture Synthesis. input image. Given a finite sample of some texture, the goal is to synthesize other samples from that same texture. The sample needs to be "large enough". SYNTHESIS.

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Introduction to Inference for Bayesian Netoworks

Introduction to Inference for Bayesian Netoworks. Robert Cowell. 2. Basic axioms of probability. Probability theory = inductive logic system of reasoning under uncertainty probability numerical measure of the degree of consistent belief in proposition Axioms P(A) = 1 iff A is certain

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Monte Carlo Artificial Intelligence: Bayesian Networks

Monte Carlo Artificial Intelligence: Bayesian Networks. Why This Matters. Bayesian networks have been the most important contribution to the field of AI in the last 10 years Provide a way to represent knowledge in an uncertain domain and a way to reason about this knowledge

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The Improved Iterative Scaling Algorithm: A gentle Introduction

The Improved Iterative Scaling Algorithm: A gentle Introduction. Adam Berger, CMU, 1997. Introduction. Random process Produces some output value y , a member of a (necessarily finite) set of possible output values

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Chapter 5 Foundations of Bayesian Networks

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Robust Bayesian Classifier

Robust Bayesian Classifier. Presented by Chandrasekhar Jakkampudi. Classification. Classification consists of assigning a class label to a set of unclassified cases. 1. Supervised Classification The set of possible classes is known in advance. 2. Unsupervised Classification

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Lecture on Bayesian Belief Networks (Basics)

Lecture on Bayesian Belief Networks (Basics). Patrycja Gradowska Open Risk Assessment Workshop Kuopio, 2009. Lecture contents. Brief introduction to BBN and example Definition of BBN Purpose of BBN Construction of BBN Concluding remarks. A simple example. Problem:

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Intelligent Systems (AI-2) Computer Science cpsc422 , Lecture 31 Mar, 31, 2014

Intelligent Systems (AI-2) Computer Science cpsc422 , Lecture 31 Mar, 31, 2014. Slide source: from David Page (MIT) (which were from From Lise Getoor , Nir Friedman, Daphne Koller , and Avi Pfeffer ) and from Lise Getoor. Lecture Overview.

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