Название: Introduction to Artificial Intelligence 3rd Edition
Автор: Wolfgang Ertel
Издательство: Springer
Серия: Undergraduate Topics in Computer Science
Год: 2025
Страниц: 393
Язык: английский
Формат: pdf (true)
Размер: 12.9 MB

This accessible and engaging textbook presents a concise introduction to the exciting field of Artificial Intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, Machine Learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated third edition also includes new material on Deep Learning.

Besides corrections of errors, this edition has new sections on data normalization, quality metrics for Machine Learning, data augmentation and a detailed introduction to convolutional networks, the now dominant technique of Deep Learning. Also new are an introduction to large language models such as ChatGPT and the GAN networks used for generative AI, followed by an assessment of how Deep Learning will change the world. And of course, the new sections come with exercises.

Machine Learning has established itself and is now the dominant AI method in every field of application. By contrast, logic no longer plays a significant role in AI. Consequently, many readers will have little interest in Chaps. 1 to 5. It is easily possible to start with Chap. 6 on search algorithms. It is also possible to start directly with machine learning in Chap. 8. However there are, for example with the Naive Bayes algorithm, some dependencies on Chap. 7.

Anyone who wants to put AI into practice should come equipped with firm knowledge of programming. Python is a useful language for this due to its relatively high level of abstraction and its many good libraries, particularly for Machine Learning. The topics of data normalization, quality metrics for classifiers, data augmentation, logistic regression, and convolutional neural networks with examples in Python have been added to Chaps. 8 and 9.

Topics and features:

· Presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website
· Introduces convolutional neural networks as the currently most important type of deep learning networks with applications to image classification (NEW)
· Contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons
· Reports on developments in deep learning, including applications of neural networks to large language models as used in state-of-the-art chatbots as well as to the generation of music and art (NEW)
· Includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks, and reinforcement learning
· Covers various classical machine learning algorithms and introduces important general concepts such as cross validation, data normalization, performance metricsand data augmentation (NEW)
· Includes a section on AI and society, discussing the implications of AI on topics such as employment and transportation






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