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CS 561: Artificial Intelligence 1 CS 561: Artificial Intelligence Instructors: Prof. Laurent Itti (itti@usc.edu) TAs: Lectures: Online & OHE-100B, Mon & Wed, 12:30 – 14:20 Office hours: Mon 14:30 – 16:00, HNB-07A (Prof. Itti) This class will use courses.uscden.net (Desire2Learn, D2L) – Up to date information, lecture notes, lecture videos – Homeworks posting and submission […]

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Computers and Creativity Jon McCormack r Mark d’Inverno Editors Computers and Creativity Editors Jon McCormack Faculty of Information Technology Monash University Caulfield East, Victoria Australia Mark d’ Inverno Computing Department Goldsmiths, University of London New Cross, London UK ISBN 978-3-642-31726-2 DOI 10.1007/978-3-642-31727-9 Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2012946745 ACM

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CS代考计算机代写 database Hidden Markov Mode scheme Excel information theory Bayesian decision tree AI Hive algorithm In Cambridge Handbook of Intelligence (3rd Edition), R.J. Sternberg & S.B. Kaufman (Editors), 2011.

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In Cambridge Handbook of Intelligence (3rd Edition), R.J. Sternberg & S.B. Kaufman (Editors), 2011. Artificial Intelligence Ashok K. Goel School of Interactive Computing Georgia Institute of Technology goel@cc.gatech.edu Jim Davies Institute of Cognitive Science Carleton University jim@jimdavies.org Introduction Artificial intelligence (AI) is the field of research that strives to understand, design and build cognitive systems.

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1 INTRODUCTION CHAPTER CHAPTER 2 INTELLIGENT AGENTS function TABLE-DRIVEN-AGENT(percept) returns an action persistent: percepts, a sequence, initially empty table, a table of actions, indexed by percept sequences, initially fully specified append percept to the end of percepts action ←LOOKUP(percepts,table) return action Figure 2.7 The TABLE-DRIVEN-AGENT program is invoked for each new percept and re- turns

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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University February 23, 2015 Today: • Graphical models • Bayes Nets: • Representing distributions • Conditional independencies • Simple inference • Simple learning Readings: • Bishop chapter 8, through 8.2 • Mitchell chapter 6 Bayes Nets define Joint Probability Distribution in terms of this

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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University February 18, 2015 Today: • Graphical models • Bayes Nets: • Representing distributions • Conditional independencies • Simple inference • Simple learning Readings: • Bishop chapter 8, through 8.2 Graphical Models • Key Idea: – Conditional independence assumptions useful – but Naïve Bayes

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Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: • What is machine learning? • Decisiontreelearning • Courselogistics Readings: • “The Discipline of ML” • Mitchell,Chapter3 • Bishop,Chapter14.4 Machine Learning: Study of algorithms that • improve their performance P • at some task T • with experience E

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Active Learning Literature Survey Burr Settles Computer Sciences Technical Report 1648 University of Wisconsin–Madison Updated on: January 26, 2010 Abstract The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns. An active learner

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lecture09.pptx LECTURE 9 Sequence Classifcaaon and Part-Of-Speech Tagging Arkaitz Zubiaga, 5 th February, 2018 2  Sequence Classifcaaon  Sequence Classifers:  Hidden Markov Models (HMM).  Maximum Entropy Markov Models (MEMM).  Condiaonal Random Fields (CRF).  Using Sequence Classifers for Part-of-Speech (POS) Tagging. LECTURE 9: CONTENTS 3  Someames, classifcaatin tif items in

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