Ai Algorithms Foundations Applications And Advancements
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AI Algorithms: Foundations, Applications, and Advancements
This comprehensive volume offers an in-depth exploration of artificial intelligence algorithms, structured into five core parts. Beginning with foundational concepts, it introduces symbolic and statistical AI, emphasizing mathematical underpinnings such as linear algebra, probability, and optimization. Classical AI techniques like search algorithms and constraint satisfaction are explored in depth before transitioning into the domain of machine learning. In supervised and unsupervised learning chapters, readers gain insights into regression, classification, clustering, and dimensionality reduction. More advanced topics such as ensemble methods, neural networks—including CNNs, RNNs, and transformers—are detailed with practical and theoretical rigor. Reinforcement learning is examined through frameworks like MDPs, Q-learning, and policy gradients. The book further delves into evolutionary and probabilistic algorithms, detailing genetic strategies, swarm intelligence, Bayesian networks, and Monte Carlo methods. Applications in natural language processing and computer vision—covering chatbots, object detection, and GANs—are presented with modern techniques like AutoML, neural architecture search, and transfer learning. A dedicated section on applications and ethics discusses real-world AI use in healthcare, finance, and robotics, along with the challenges of bias, explainability, and governance. Finally, the book explores future directions: the quest for AGI, the promise of quantum AI, and the transformative impact of AI on labor and society. Balancing technical depth with clarity, this book serves as a valuable resource for students, practitioners, and researchers seeking a robust understanding of both the fundamentals and frontiers of AI.
Knowledge Graphs for Explainable Artificial Intelligence: Foundations, Applications and Challenges
The latest advances in Artificial Intelligence and (deep) Machine Learning in particular revealed a major drawback of modern intelligent systems, namely the inability to explain their decisions in a way that humans can easily understand. While eXplainable AI rapidly became an active area of research in response to this need for improved understandability and trustworthiness, the field of Knowledge Representation and Reasoning (KRR) has on the other hand a long-standing tradition in managing information in a symbolic, human-understandable form. This book provides the first comprehensive collection of research contributions on the role of knowledge graphs for eXplainable AI (KG4XAI), and the papers included here present academic and industrial research focused on the theory, methods and implementations of AI systems that use structured knowledge to generate reliable explanations. Introductory material on knowledge graphs is included for those readers with only a minimal background in the field, as well as specific chapters devoted to advanced methods, applications and case-studies that use knowledge graphs as a part of knowledge-based, explainable systems (KBX-systems). The final chapters explore current challenges and future research directions in the area of knowledge graphs for eXplainable AI. The book not only provides a scholarly, state-of-the-art overview of research in this subject area, but also fosters the hybrid combination of symbolic and subsymbolic AI methods, and will be of interest to all those working in the field.
Artificial Intelligence and Machine Learning Fundamentals
Create AI applications in Python and lay the foundations for your career in data science Key FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail with interesting examplesMaster core AI concepts with engaging activitiesBook Description Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills! What you will learnUnderstand the importance, principles, and fields of AIImplement basic artificial intelligence concepts with PythonApply regression and classification concepts to real-world problemsPerform predictive analysis using decision trees and random forestsCarry out clustering using the k-means and mean shift algorithmsUnderstand the fundamentals of deep learning via practical examplesWho this book is for Artificial Intelligence and Machine Learning Fundamentals is for software developers and data scientists who want to enrich their projects with machine learning. You do not need any prior experience in AI. However, it’s recommended that you have knowledge of high school-level mathematics and at least one programming language (preferably Python).