PowerPoint Slides for Negnevitsky: Artificial Intelligence, 3e
Artificial Intelligence is one of the most rapidly evolving subjects within the computing/engineering curriculum, with an emphasis on creating practical applications from hybrid techniques. Despite this, the traditional textbooks continue to expect mathematical and programming expertise beyond the scope of current undergraduates and focus on areas not relevant to many of today's courses. Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also data mining.
The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. However, available tools and their uses will be described and program examples will be given in MATLAB. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contemporary coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques.
The book covers:
- Rule-based expert systems
- Fuzzy expert systems
- Frame-based expert systems
- Artificial neural networks
- Evolutionary computation
- Hybrid intelligent systems
- Knowledge engineering
- Data mining
This product is an alternate version of
|Title||Artificial Intelligence 3e e-book: A Guide to Intelligent Systems|
|Online purchase price||£46.00|
This book covers many areas related to my module. I would be happy to recommend this book to my students. I believe my students would be able to follow this book without any difficulty. Book chapters are very well organised and this will help me to pick and choose the subjects related to this module. Dr Ahmad Lotfi, Nottingham Trent University, UK
- No mathematical or programming prerequisites.
- Linked coverage of all the latest artificial intelligence topics.
- Question and answer format.
- Accompanying website including student projects, accompanying software tools, software demonstrations, PowerPoint slides and solutions to exercises.
New to This Edition
The main objective of the book remains the same as in the first edition to provide the reader with practical understanding of the field of computer intelligence. It is intended as an introductory text suitable for a one-semester course, and assumes the students have only limited knowledge of calculus and little or no programming experience.
In terms of the coverage, this edition introduces a new chapter on data mining and demonstrates several new applications of intelligent tools for solving complex real-world problems. The major changes are as follows:
· In the new chapter, Data mining and knowledge discovery, we introduce data mining as an integral part of knowledge discovery in large databases. We consider the main techniques and tools for turning data into knowledge, including statistical methods, data visualisation tools, Structured Query Language, decision trees and market basket analysis. We also present several case studies on data mining applications.
· In Chapter 9, we add a new case study on clustering with a self-organising neural network.
Finally, we have expanded the books references and bibliographies, and updated the list of AI tools and vendors in the appendix.
Table of Contents
New to this edition xiii
Overview of the book xiv
1 Introduction to knowledge-based intelligent systems 1
1.1 Intelligent machines, or what machines can do 1
1.2 The history of artificial intelligence, or from the Dark Ages
to knowledge-based systems 4
1.3 Summary 17
Questions for review 21
2 Rule-based expert systems 25
2.1 Introduction, or what is knowledge? 25
2.2 Rules as a knowledge representation technique 26
2.3 The main players in the expert system development team 28
2.4 Structure of a rule-based expert system 30
2.5 Fundamental characteristics of an expert system 33
2.6 Forward chaining and backward chaining inference
2.7 MEDIA ADVISOR: a demonstration rule-based expert system 41
2.8 Conflict resolution 47
2.9 Advantages and disadvantages of rule-based expert systems 50
2.10 Summary 51
Questions for review 53
3 Uncertainty management in rule-based expert systems 55
3.1 Introduction, or what is uncertainty? 55
3.2 Basic probability theory 57
3.3 Bayesian reasoning 61
3.4 FORECAST: Bayesian accumulation of evidence 65
3.5 Bias of the Bayesian method 72
3.6 Certainty factors theory and evidential reasoning 74
3.7 FORECAST: an application of certainty factors 80
3.8 Comparison of Bayesian reasoning and certainty factors 82
3.9 Summary 83
Questions for review 85
4 Fuzzy expert systems 87
4.1 Introduction, or what is fuzzy thinking? 87
4.2 Fuzzy sets 89
4.3 Linguistic variables and hedges 94
4.4 Operations of fuzzy sets 97
4.5 Fuzzy rules 103
4.6 Fuzzy inference 106
4.7 Building a fuzzy expert system 114
4.8 Summary 125
Questions for review 126
5 Frame-based expert systems 131
5.1 Introduction, or what is a frame? 131
5.2 Frames as a knowledge representation technique 133
5.3 Inference in frame-based experts 138
5.4 Methods and demons 142
5.5 Interaction of frames and rules 146
5.6 Buy Smart: a frame-based expert system 149
5.7 Summary 161
Questions for review 163
6 Artificial neural networks 165
6.1 Introduction, or how the brain works 165
6.2 The neuron as a simple computing element 168
6.3 The perceptron 170
6.4 Multilayer neural networks 175
6.5 Accelerated learning in multilayer neural networks 185
6.6 The Hopfield network 188
6.7 Bidirectional associative memories 196
6.8 Self-organising neural networks 200
6.9 Summary 212
Questions for review 215
7 Evolutionary computation 219
7.1 Introduction, or can evolution be intelligent? 219
7.2 Simulation of natural evolution 219
7.3 Genetic algorithms 222
7.4 Why genetic algorithms work 232
7.5 Case study: maintenance scheduling with genetic
7.6 Evolutionary strategies 242
7.7 Genetic programming 245
7.8 Summary 254
Questions for review 255
8 Hybrid intelligent systems 259
8.1 Introduction, or how to combine German mechanics
with Italian love 259
8.2 Neural expert systems 261
8.3 Neuro-fuzzy systems 268
8.4 ANFIS: Adaptive Neuro-Fuzy Inference System 277
8.5 Evolutionary neural networks 285
8.6 Fuzzy evolutionary systems 290
8.7 Summary 296
Questions for review 297
9 Knowledge engineering 301
9.1 Introduction, or what is knowledge engineering? 301
9.2 Will an expert system work for my problem? 308
9.3 Will a fuzzy expert system work for my problem? 317
9.4 Will a neural network work for my problem? 323
9.5 Will genetic algorithms work for my problem?
9.6 Will a hybrid intelligent system work for my problem?
Questions for review
10 Data mining and knowledge discovery
10.1 Introduction, or what is data mining?
10.2 Statistical methods and data visualisation
10.3 Principal components analysis
10.4 Relational databases and database queries
10.5 The data warehouse and multidimensional data analysis
10.6 Decision trees
10.7 Association rules and market basket analysis
Questions for review
PowerPoint Slides for Negnevitsky: Artificial Intelligence, 3e
Websites and online courses
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About the Author(s)
Dr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. The book has developed from his lectures to undergraduates. Educated as an electrical engineer, Dr Negnevitskys many interests include artificial intelligence and soft computing. His research involves the development and application of intelligent systems in electrical engineering, process control and environmental engineering. He has authored and co-authored over 300 research publications including numerous journal articles, four patents for inventions and two books.
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