Artificial Intelligence: Foundations of Computational Agents (3版)
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【簡介】
Introduces concepts at the right level of detail, using straightforward language and minimum mathematics
Examines the social and ethical impacts of AI, connecting techniques to real-world benefits and harms
Explains state-of-the-art algorithms and examples underlying the theory, demonstrating how concepts are applied
Provides two complementary software systems – AIPython and AILog - for experimentation and extension
Pedagogical features include examples, end-of-chapter reviews, further reading lists, and exercises
【目錄】
Part I - Agents in the World
Chapter 1 - Artificial Intelligence and Agents
Chapter 2 - Agent Architectures and Hierarchical Control
Part II - Reasoning and Planning with Certainty
Chapter 3 - Searching for Solutions
Chapter 4 - Reasoning with Constraints
Chapter 5 - Propositions and Inference
Chapter 6 - Deterministic Planning
Part III - Learning and Reasoning with Uncertainty
Chapter 7 - Supervised Machine Learning
Chapter 8 - Neural Networks and Deep Learning
Chapter 9 - Reasoning with Uncertainty
Chapter 10 - Learning with Uncertainty
Chapter 11 - Causality
Part IV - Planning and Acting with Uncertainty
Chapter 12 - Planning with Uncertainty
Chapter 13 - Reinforcement Learning
Chapter 14 - Multiagent Systems
Part V - Representing Individuals and Relations
Chapter 15 - Individuals and Relations
Chapter 16 - Knowledge Graphs and Ontologies
Chapter 17 - Relational Learning and Probabilistic Reasoning
Part VI - The Big Picture
Chapter 18 - The Social Impact of Artificial Intelligence
Chapter 19 - Retrospect and Prospect
Appendix A - Mathematical Preliminaries and Notation
Appendix B - Mapping to Open-Source Packages
原價:
1480
售價:
1376
現省:
104元
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Foundations of Computer Vision (Adaptive Computation and Machine Learning series) (1版)
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【簡介】
An accessible, authoritative, and up-to-date computer vision textbook offering a comprehensive introduction to the foundations of the field that incorporates the latest deep learning advances.
Machine learning has revolutionized computer vision, but the methods of today have deep roots in the history of the field. Providing a much-needed modern treatment, this accessible and up-to-date textbook comprehensively introduces the foundations of computer vision while incorporating the latest deep learning advances. Taking a holistic approach that goes beyond machine learning, it addresses fundamental issues in the task of vision and the relationship of machine vision to human perception. Foundations of Computer Vision covers topics not standard in other texts, including transformers, diffusion models, statistical image models, issues of fairness and ethics, and the research process. To emphasize intuitive learning, concepts are presented in short, lucid chapters alongside extensive illustrations, questions, and examples. Written by leaders in the field and honed by a decade of classroom experience, this engaging and highly teachable book offers an essential next-generation view of computer vision.
Up-to-date treatment integrates classic computer vision and deep learning
Accessible approach emphasizes fundamentals and assumes little background knowledge
Student-friendly presentation features extensive examples and images
Proven in the classroom
Instructor resources include slides, solutions, and source code
【目錄】
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