L16 – Deep Belief Networks EECS 391 Intro to AI Deep Belief Networks L16 Thu Nov 2 Michael S. Lewicki ◇ CWRUEECS 531: Computer Vision Hierarchy of brain areas in the mammalian visual system Flat map of macaque monkey brain Hierarchy of brain areas from Felleman and Van Essen (1991)Simple and Complex Cells are here […]
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L14 – Inference in Bayes Nets EECS 391 Intro to AI Inference in Bayes Nets L14 Thu Oct 25 Recap: Modeling causal relationships with belief networks Direct cause A B Indirect cause A B C Common cause Common effect A B C A B C P(B|A) P(B|A) P(C|B) P(B|A) P(C|A) P(C|A,B) Defining the belief network
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Bayesian networks CISC 6525 Bayesian Networks Chapter 14 Outline Syntax Semantics Efficient representations Inference Bayesian networks A simple, graphical notation for conditional independence assertions and hence for compact specification of full joint distributions Syntax: a set of nodes, one per variable a directed, acyclic graph (link ≈ “directly influences”) a conditional distribution for each node
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0132642824.pdf Artificial Intelligence A Modern Approach Third Edition PRENTICE HALL SERIES IN ARTIFICIAL INTELLIGENCE Stuart Russell and Peter Norvig, Editors FORSYTH & PONCE Computer Vision: A Modern Approach GRAHAM ANSI Common Lisp JURAFSKY & MARTIN Speech and Language Processing, 2nd ed. NEAPOLITAN Learning Bayesian Networks RUSSELL & NORVIG Artificial Intelligence: A Modern Approach, 3rd ed.
Introduction to Information Retrieval Online edition (c) 2009 Cambridge UP An Introduction to Information Retrieval Draft of April 1, 2009 Online edition (c) 2009 Cambridge UP Online edition (c) 2009 Cambridge UP An Introduction to Information Retrieval Christopher D. Manning Prabhakar Raghavan Hinrich Schütze Cambridge University Press Cambridge, England Online edition (c) 2009 Cambridge UP
WEKA Manual for Version 3-6-13 Remco R. Bouckaert Eibe Frank Mark Hall Richard Kirkby Peter Reutemann Alex Seewald David Scuse September 9, 2015 c©2002-2015 University of Waikato, Hamilton, New Zealand Alex Seewald (original Commnd-line primer) David Scuse (original Experimenter tutorial) This manual is licensed under the GNU General Public License version 2. More information about
ENVM 3503 Semester 1 2003 1 MGTS7526 Assignment 2 – Risk Modelling Assignment Sheet The total length of your assignment should not exceed eight (8) pages. 1. Horse Race (10 marks) Let’s assume that there is a race between two horses: Fleetfoot and Dogmeat, and you want to determine which horse to bet on. Fleetfoot
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CISC 6525 Artificial Intelligence CISC 6525 Artificial Intelligence Time and Uncertainty Chapter 15 Temporal Probabilistic Agent environment agent ? sensors actuators t1, t2, t3, … Time and Uncertainty The world changes, we need to track and predict it Examples: diabetes management, traffic monitoring Basic idea: copy state and evidence variables for each time step Xt
0132642824.pdf Artificial Intelligence A Modern Approach Third Edition PRENTICE HALL SERIES IN ARTIFICIAL INTELLIGENCE Stuart Russell and Peter Norvig, Editors FORSYTH & PONCE Computer Vision: A Modern Approach GRAHAM ANSI Common Lisp JURAFSKY & MARTIN Speech and Language Processing, 2nd ed. NEAPOLITAN Learning Bayesian Networks RUSSELL & NORVIG Artificial Intelligence: A Modern Approach, 3rd ed.
Microsoft Word – liub-SA-and-OM-book Sentiment Analysis and Opinion Mining April 22, 2012 Bing Liu liub@cs.uic.edu Draft: Due to copyediting, the published version is slightly different Bing Liu. Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, May 2012. Sentiment Analysis and Opinion Mining 2 Table of Contents Preface ………………………………………………………………………………….5 Sentiment Analysis: A Fascinating Problem ……………………………..7