Artificial Intelligence: A Guide to Intelligent Systems, 4e
These fundamental questions are clearly answered by Michael Negnevitsky’s Artificial Intelligence: A Guide to Intelligent Systems. Unlike many texts burdened with complex matrix algebra and differential equations, this book demonstrates that the core ideas behind intelligent systems are simple and straightforward.
This text assumes little or no prior programming experience as it expertly tackles topics like:
- Expert Systems
- Fuzzy Systems
- Artificial Neural Network
- Evolutionary Computation
- Knowledge Engineering
- Data Mining
New and Updated Features:
- New Chapter on Deep Learning: Examines different architectures of deep learning and convolutional neural networks, identifying common features of deep neural networks.
- Generative AI: A new section explores generative AI and discusses contemporary chatbots like Alexa, Siri, and ChatGPT.
- Semantic Networks: A new section discusses successful applications of the semantic web, including improved data management and enhanced search capabilities.
- Reinforcement Learning: Introduces the concepts of model-based and model-free reinforcement learning.
- Real-World Case Studies: Two new case studies focus on image recognition using a convolutional neural network and finding the optimum of an unknown function using particle swarm optimisation.
| Book | |
|---|---|
| Author | Negnevitsky |
| Pages | 600 |
| Year | 2025 |
| ISBN | 9789367133170 |
| Publisher | Pearson |
| Language | English |
| Uncategorized | |
| Edition | 4/e |
| Weight | 720 g |
| Dimensions | 23.5 x 17.2 x 2.3 cm |
| Binding | Paperback |