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This book introduces the basic methodologies for successful data analytics. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.
This book is a comprehensive collection of extended contributions from the Workshops on Computational Optimization 2019.Our everyday life is unthinkable without optimization.
This book introduces the state-of-the-art algorithms for data and computation privacy.
To distinguish this textbook from others, the author considers probabilistic methods as being fundamental for the construction of simple and efficient algorithms, and in each chapter at least one problem is solved using a randomized algorithm.
This book offers a self-contained guide to advanced algorithms and their applications in various fields of science.
This book features research presented and discussed during the Research & Innovation Forum (Rii Forum) 2020.
This textbook introduces major topics that include quantum bits, superposition, entanglement, logic gates, quantum search algorithm, quantum Fourier transform, inverse quantum Fourier transform, Shor’s order-finding algorithm and phase estimation.
This book uses new mathematical tools to examine broad computability and complexity questions in enumerative combinatorics, with applications to other areas of mathematics, theoretical computer science, and physics.