Provides a comprehensive introduction to the mathematical foundations of data science for students and self-learners. The book covers sets, functions, linear algebra, matrix decomposition, calculus, probability, and statistics, and demonstrates how these concepts underpin modern data science algorithms such as principal component analysis, singular value decomposition, linear regression, neural networks, maximum likelihood estimation, logistic regression, and ridge regression. Numerous worked examples and exercises help readers develop both theoretical understanding and practical problem-solving skills.

Examines how blockchain technology can be integrated with artificial intelligence to improve trust, transparency, security, and accountability in AI systems. The book explores the use of distributed ledger technology to address challenges such as hallucinations, bias, data integrity, privacy, and governance in AI applications. It also presents practical architectures, enterprise use cases, and emerging trends for developing secure and responsible AI solutions across multiple industries.

Provides a comprehensive introduction to the theory and practice of artificial intelligence, integrating its mathematical, computational, philosophical, and engineering foundations. The book covers symbolic AI, search techniques, knowledge representation, expert systems, neural networks, deep learning, probabilistic reasoning, large language models, and ethical issues in AI. Designed for undergraduate and graduate students, it includes numerous examples, exercises, and practical applications to support learning.

Provides an accessible introduction to the relationship between artificial intelligence and brain research. The book explains the principles of neural networks, deep learning, and cognitive science, explores how advances in neuroscience have influenced AI development, and examines how AI contributes to understanding the human brain. It also discusses current challenges, emerging technologies, and the future of intelligent systems and cognition.

This book provides a thorough introduction to Python, specifically designed for those in business analytics. It starts with the fundamentals of Python and gradually covers more advanced topics, including data manipulation, visualization, and analytics techniques. The content is structured to help readers build a strong foundation in Python, essential for success in data science and business analytics. The book also features real-world case studies and practical examples, demonstrating how Python can be applied in business decision-making. These insights make it a valuable resource for students and professionals who want to use Python to solve real business problems. Python's importance in today’s data-driven industries cannot be overstated. Proficiency in this programming language enhances the ability to tackle complex challenges and supports strategic decision-making. For organizations, Python enables the setting of data-driven goals, improved performance, and the fostering of continuous learning. Its open-source nature and wide range of online resources make it accessible to everyone, ensuring that users are equipped with the skills needed in a rapidly evolving workplace. This book serves as a comprehensive guide for those aiming to excel in the field of business analytics through the effective use of Python

How the computer revolution shaped our conception of rationality—and why human problems require solutions rooted in human intuition, morality, and judgment In the 1940s, mathematicians set out to design computers that could act as ideal rational agents in the face of uncertainty. The Irrational Decision tells the story of how they settled on a peculiar mathematical definition of rationality in which every decision is a statistical question of risk. Benjamin Recht traces how this quantitative standard came to define our understanding of rationality, looking at the history of optimization, game theory, statistical testing, and machine learning. He explains why, now more than ever, we need to resist efforts by powerful tech interests to drive public policy and essentially rule our lives. While mathematical rationality has proven valuable in accelerating computers, regulating pharmaceuticals, and deploying electronic commerce, it fails to solve messy human problems and has given rise to a view of a rational world that is not only overquantified but surprisingly limited. Recht shows how these mathematical methods emerged from wartime research and influenced fields ranging from economics to health care, drawing on illuminating examples ranging from diet planning to chess to self-driving cars. Highlighting both the power and limitations of mathematical rationality, The Irrational Decision reveals why only humans can resolve fundamentally political or value-based questions and proposes a more expansive approach to decision making that is appropriately supported by computational tools yet firmly rooted in human intuition, morality, and judgment.