Miroslav Kubat – författare

A hands-on introduction to the principles and practices of modern artificial intelligence

This comprehensive textbook focuses on the core techniques and processes underlying today’s artificial intelligence, including algorithms, data structures, logic, automated reasoning, and problem solving. The book contains information about planning and about expert systems.

Fundamentals of Artificial Intelligence: Problem Solving and Automated Reasoning is written in a concise format with a view to optimizing learning. Each chapter contains a brief historical overview, control questions to reinforce important concepts, plus computer assignments and ideas for independent thought. The book includes many visuals to illustrate the essential ideas and many examples to show how to use these ideas in practical implementations.

A hands-on introduction to the principles and practices of modern artificial intelligence

This comprehensive textbook focuses on the core techniques and processes underlying today’s artificial intelligence, including algorithms, data structures, logic, automated reasoning, and problem solving. The book contains information about planning and about expert systems.

Fundamentals of Artificial Intelligence: Problem Solving and Automated Reasoning is written in a concise format with a view to optimizing learning. Each chapter contains a brief historical overview, control questions to reinforce important concepts, plus computer assignments and ideas for independent thought. The book includes many visuals to illustrate the essential ideas and many examples to show how to use these ideas in practical implementations.

This textbook offers a comprehensive introduction to Machine Learning techniques and algorithms. This Third Edition covers newer approaches that have become highly topical, including deep learning, and auto-encoding, introductory information about temporal learning and hidden Markov models, and a much more detailed treatment of reinforcement learning. The book is written in an easy-to-understand manner with many examples and pictures, and with a lot of practical advice and discussions of simple applications. 

The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, rule-induction programs, artificial neural networks, support vector machines, boosting algorithms, unsupervised learning (including Kohonen networks and auto-encoding), deep learning, reinforcement learning, temporal learning (including long short-term memory), hidden Markov models, and the genetic algorithm. Special attention is devoted to performance evaluation, statistical assessment, and to many practical issues ranging from feature selection and feature construction to bias, context, multi-label domains, and the problem of imbalanced classes.

This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.