Search results for "Introduction to Statistical Data Science"
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Algebraic Geometry and Statistical Learning Theory
Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process theory on algebraic varieties.
R 4,120.04
Computational Learning Theory
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
R 2,306.74
Artificial Intelligence
This book presents contextualised and detailed research on Artificial Intelligence (AI) and the Global South. It examines the key challenges of these emerging and least industrialised countries while proffering holistic and comprehensive solutions. The book then explains how AI, as part of these broad interventions, can drive Global South economies to achieve inclusive development and shared prosperity. The book outlines how countries can swiftly prepare to adopt and develop AI across all sectors. It presents novel national, regional, and continental AI adoption, development, and implementation frameworks.Features:Broad non-AI interventions and prescriptions to address Global South challengesA comprehensive but accessible introduction to AI concepts, technology, infrastructure, systems, and innovations such as AlphaFold, ChatGPT-4, and DeepSeek-R1An overview of AI-related technologies such as quantum computing, battery energy storage systems, 3D printing, nanotechnology, IoT, and blockchainHow to prepare emerging economies to unlock the benefits of AI while mitigating the risksDiscussion of specific AI applications in 11 critical Global South sectorsDetails of 11 sector case studies of AI adoption in the Global South and Global NorthTen country case studies: Sharing emergent AI experiences in the Global SouthAI adoption framework: vision, strategy, policy, governance, legislation/regulation, and implementation matrixA framework for democratising and decolonising AIThe value proposition for AI research, development, and ownership in the Global SouthA case for the participation of the Global South in the AI semiconductor industryThis book is aimed at policymakers, business leaders, graduate students, academics, researchers, strategic thinkers, and world leaders seeking to understand and leverage the transformative role of AI-based systems in achieving inclusive development, economic transformation, and shared prosperity.
R 2,711.00
