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ARTIFICIAL INTELLIGENCE

WITH AN INTRODUCTION TO MACHINE LEARNING, SECOND EDITION

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9781138502383 ::  ARTIFICIAL INTELLIGENCE
ISBN:

9781138502383

Colección:CHAPMAN & HALL/CRC ARTIFICIAL INTELLIGENCE AND ROBOTICS SERIES
EditorialTAYLOR & FRANCIS GROUP
Edicion:
Páginas:480
Idioma:INGLES
P.V.P.: 119,75 € + 4% IVA = 124,54 €
Dto 5% Ahorras 6,23 €
Importe final iva incl. 118,31 €
PLAZO DE ENTREGA 20 DIAS

<P>THE FIRST EDITION OF THIS POPULAR TEXTBOOK, <B><I>CONTEMPORARY ARTIFICIAL INTELLIGENCE</B></I>, PROVIDED AN ACCESSIBLE AND STUDENT FRIENDLY INTRODUCTION TO AI. THIS FULLY REVISED AND EXPANDED UPDATE, <B><I>ARTIFICIAL INTELLIGENCE: WITH AN INTRODUCTION TO MACHINE LEARNING, SECOND EDITION, </B></I>RETAINS THE SAME ACCESSIBILITY AND PROBLEM-SOLVING APPROACH, WHILE PROVIDING NEW MATERIAL AND METHODS.</P><P>THE BOOK IS DIVIDED INTO FIVE SECTIONS THAT FOCUS ON THE MOST USEFUL TECHNIQUES THAT HAVE EMERGED FROM AI. THE FIRST SECTION OF THE BOOK COVERS LOGIC-BASED METHODS, WHILE THE SECOND SECTION FOCUSES ON PROBABILITY-BASED METHODS. EMERGENT INTELLIGENCE IS FEATURED IN THE THIRD SECTION AND EXPLORES EVOLUTIONARY COMPUTATION AND METHODS BASED ON SWARM INTELLIGENCE. THE NEWEST SECTION COMES NEXT AND PROVIDES A DETAILED OVERVIEW OF NEURAL NETWORKS AND DEEP LEARNING. THE FINAL SECTION OF THE BOOK FOCUSES ON NATURAL LANGUAGE UNDERSTANDING.</P><P>SUITABLE FOR UNDERGRADUATE AND BEGINNING GRADUATE STUDENTS, THIS CLASS-TESTED TEXTBOOK PROVIDES STUDENTS AND OTHER READERS WITH KEY AI METHODS AND ALGORITHMS FOR SOLVING CHALLENGING PROBLEMS INVOLVING SYSTEMS THAT BEHAVE INTELLIGENTLY IN SPECIALIZED DOMAINS SUCH AS MEDICAL AND SOFTWARE DIAGNOSTICS, FINANCIAL DECISION MAKING, SPEECH AND TEXT RECOGNITION, GENETIC ANALYSIS, AND MORE.</P>

<P>1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE <BR>1.1 HISTORY OF ARTIFICIAL INTELLIGENCE <BR>1.2 OUTLINE OF THIS BOOK </P><P></P><P>PART I LOGICAL INTELLIGENCE </P><P>2. PROPOSITIONAL LOGIC <BR>2.1 BASICS OF PROPOSITIONAL LOGIC <BR>2.2 RESOLUTION <BR>2.3 ARTIFICIAL INTELLIGENCE APPLICATIONS <BR>2.4 DISCUSSION AND FURTHER READING </P><P></P><P>3. FIRST-ORDER LOGIC <BR>3.1 BASICS OF FIRST-ORDER LOGIC <BR>3.2 ARTIFICIAL INTELLIGENCE APPLICATIONS <BR>3.3 DISCUSSION AND FURTHER READING </P><P>4. CERTAIN KNOWLEDGE REPRESENTATION <BR>4.1 TAXONOMIC KNOWLEDGE <BR>4.2 FRAMES <BR>4.3 NONMONOTONIC LOGIC <BR>4.4 DISCUSSION AND FURTHER READING </P><P></P><P>5. LEARNING DETERMINISTIC MODELS <BR>5.1 SUPERVISED LEARNING <BR>5.2 REGRESSION <BR>5.3 PARAMETER ESTIMATION <BR>5.4 LEARNING A DECISION TREE </P><P></P><P>PART II PROBABILISTIC INTELLIGENCE </P><P>6. PROBABILITY <BR>6.1 PROBABILITY BASICS <BR>6.2 RANDOMVARIABLES <BR>6.3 MEANING OF PROBABILITY <BR>6.4 RANDOMVARIABLES IN APPLICATIONS <BR>6.5 PROBABILITY IN THE WUMPUS WORLD </P><P></P><P>7. UNCERTAIN KNOWLEDGE REPRESENTATION <BR>7.1 INTUITIVE INTRODUCTION TO BAYESIAN NETWORKS <BR>7.2 PROPERTIES OF BAYESIAN NETWORKS <BR>7.3 CAUSAL NETWORKS AS BAYESIAN NETWORKS <BR>7.4 INFERENCE IN BAYESIAN NETWORKS <BR>7.5 NETWORKS WITH CONTINUOUS VARIABLES <BR>7.6 OBTAINING THE PROBABILITIES <BR>7.7 LARGE-SCALE APPLICATION: PROMEDAS </P><P></P><P>8. ADVANCED PROPERTIES OF BAYESIAN NETWORK <BR>8.1 ENTAILED CONDITIONAL INDEPENDENCIES <BR>8.2 FAITHFULNESS <BR>8.3 MARKOV EQUIVALENCE <BR>8.4 MARKOV BLANKETS AND BOUNDARIES </P><P></P><P>9. DECISION ANALYSIS <BR>9.1 DECISION TREES <BR>9.2 INFLUENCE DIAGRAMS <BR>9.3 MODELING RISK PREFERENCES <BR>9.4 ANALYZING RISK DIRECTLY <BR>9.5 GOOD DECISION VERSUS GOOD OUTCOME <BR>9.6 SENSITIVITY ANALYSIS <BR>9.7 VALUE OF INFORMATION <BR>9.8 DISCUSSION AND FURTHER READING </P><P></P><P>10. LEARNING PROBABILISTIC MODEL PARAMETERS <BR>10.1 LEARNING A SINGLE PARAMETER <BR>10.2 LEARNING PARAMETERS IN A BAYESIAN NETWORK . <BR>10.3 LEARNING PARAMETERS WITH MISSING DATA </P><P></P><P>11. LEARNING PROBABILISTIC MODEL STRUCTURE <BR>11.1 STRUCTURE LEARNING PROBLEM <BR>11.2 SCORE-BASED STRUCTURE LEARNING <BR>11.3 CONSTRAINT-BASED STRUCTURE LEARNING <BR>11.4 APPLICATION: MENTOR <BR>11.5 SOFTWARE PACKAGES FOR LEARNING <BR>11.6 CAUSAL LEARNING <BR>11.7 CLASS PROBABILITY TREES <BR>11.8 DISCUSSION AND FURTHER READING </P><P></P><P>12. UNSUPERVISED LEARNING AND REINFORCEMENT LEARNING <BR>12.1 UNSUPERVISED LEARNING <BR>12.2 REINFORCEMENT LEARNING<BR>12.3 DISCUSSION AND FURTHER READING </P><P></P><P>PART III EMERGENT INTELLIGENCE </P><P>13. EVOLUTIONARY COMPUTATION <BR>13.1 GENETICS REVIEW <BR>13.2 GENETIC ALGORITHMS <BR>13.3 GENETIC PROGRAMMING<BR>13.4 DISCUSSION AND FURTHER READING </P><P></P><P>14. SWARM INTELLIGENCE <BR>14.1 ANT SYSTEM <BR>14.2 FLOCKS <BR>14.3 DISCUSSION AND FURTHER READING </P><P></P><P>PART IV NEURAL INTELLIGENCE </P><P>15. NEURAL NETWORKS AND DEEP LEARNING <BR>15.1 THE PERCEPTRON <BR>15.2 FEEDFORWARD NEURAL NETWORKS <BR>15.3 ACTIVATION FUNCTIONS <BR>15.4 APPLICATION TO IMAGE RECOGNITION <BR>15.5 DISCUSSION AND FURTHER READING </P><P></P><P>PART V LANGUAGE UNDERSTANDING </P><P>16. NATURAL LANGUAGE UNDERSTANDING <BR>16.1 PARSING <BR>16.2 SEMANTIC INTERPRETATION <BR>16.3 CONCEPT/KNOWLEDGE INTERPRETATION <BR>16.4 INFORMATION EXTRACTION <BR>16.5 DISCUSSION AND FURTHER READING </P>

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