Topics and features:
Presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website
Contains numerous study exercises and solutions, highlighted examples, definitions, theorems, and illustrative cartoons
Includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learn
ing
Reports on developments in deep learning, including applications of neural networks to generate creative content such as text, music and art (NEW)
Examines performance evaluation of clustering algorithms, and presents two practical examples explaining Bayes’ theorem and its relevance in everyday life (NEW)
Discusses search algorithms, analyzing the cycle check, explaining route planning for car navigation systems, and introducing Monte Carlo Tree Search (NEW)
Includes a section in the introduction on AI and society, discussing the implications of AI on topics such as employment and transportation (NEW)
Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.