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A Genetic Algorithm Tutorial
Covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms.
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13214 |
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Advances in Large Margin Classifiers [URL's no longer available]
The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research.
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5513 |
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Artificial Intelligence through Prolog
Teaches key concepts of Artificial Intelligence in a simple and concrete way using Prolog. Includes 500 practical programming examples in the style of programming language and data structures books.
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9708 |
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Brief Introduction to Educational Implications of Artificial Intelligence
This short book is about how humans are using artificial intelligence as an aid to solving problems and accomplishing tasks.
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3811 |
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Computational Linguistics - Models, Resources, Applications
Focuses on the basic set of ideas and facts from the fundamental science necessary for the creation of intelligent language processing tools, without going deeply into the details of specific algorithms or toy systems.
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6053 |
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Convex Optimization
This book helps the reader develop a working knowledge of convex optimization, i.e. to develop the skills and background needed to recognize, formulate, and solve convex optimization problems.
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5676 |
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Foundations of Constraint Satisfaction
A comprehensive book on the field of constraint satisfaction, the core of many applications in artificial intelligence. Covers both the theoretical and the implementation aspects of the subject.
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2661 |
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Gaussian Processes for Machine Learning
The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. Contains illustrative examples and exercises, and code and datasets are available on the Web.
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2655 |
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Global Optimization Algorithms - Theory and Application
This book elaborates on many of the basic principles in global optimization, evolutionary algorithms, and genetic programming and describes how they can efficiently be realized in software.
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6572 |
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Introduction to Machine Learning
This book surveys many of the important topics in machine learning circa 1996. The intention was to pursue a middle ground between theory and practice. It is neither a handbook of practice nor a compendium of theoretical proofs.
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7628 |
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Introduction to Neural Networks with Java
Introduces the Java programmer to the world of Neural Networks and Artificial Intelligence. Neural network architectures such as the feedforward backpropagation, Hopfield, and Kohonen networks are discussed.
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5274 |
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Machine Learning, Neural and Statistical Classification
The aim of this book is to provide a review of different approaches to classification, compare their performance on a wide range of challenging data-sets, and draw conclusions on their applicability to realistic industrial problems.
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6135 |
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Natural Language Processing in Prolog
Teaches the parsing and understanding of natural language processing and computational linguistics using Prolog.
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10027 |
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Neural Nets
Covers an introduction to neural networks in which basic concepts, techniques and ideas are given priority over lengthy mathematical proofs and technical detail.
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14270 |
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Practical Artificial Intelligence Programming in Java
This book was written for both professional programmers and home hobbyists who already know how to program in Java and who want to learn practical AI programming techniques.
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10850 |
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Probability Theory: The Logic of Science
Covers probability theory and all of its conventional mathematics in a wider context than that of the standard textbooks.
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8198 |
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Prolog and Natural-Language Analysis - Digital Edition
An introduction to elementary computational linguistics from logic programming point of view using Prolog.
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9919 |
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Reinforcement Learning: An Introduction
Provides a clear and simple account of the key ideas and algorithms of reinforcement learning. Familiarity with elementary concepts of probability is assumed.
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3389 |
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