£79.99
£79.99
Master the linear algebra skills essential for careers in AI. For postgraduates and advanced students, this text provides novel approaches to matrix theory, introducing concepts like the gradient vector and Hessian matrix used in machine learning, with key examples and exercises.
This textbook strives to provide postgraduates and advanced undergraduates majoring in both mathematics and non-mathematics with new perspectives and novel approaches. The main goal is to help students, academic researchers and research and development (R&D) specialists master the most important and essential knowledge and skills of linear algebra for conquering the scientific and technological frontier, and for using this later in their careers in the era of artificial intelligence (AI).
The text logically summarizes and reinforces the concepts, methods and conclusions of the analytic (or matrix) theory part that the reader has learned. Also, the text guides the reader to an appreciation of interrelations among different aspects and introduces some new concepts such as the gradient vector and Hessian matrix, which are often used in machine learning.
The potential knowledge gaps, such as block matrix, matrix decompositions, and generalized inverse are gradually developed, properly compared, intuitively illustrated, and thoroughly explained.
The textbook includes the feature examples that illustrate key concepts as well as exercises that strengthen understanding.
This book presents sports statistics to academics and fans alike. Even without advanced math knowledge, readers will gain completely new insights into their favourite sports by combining sports analytics, data visualisation, and advanced statistical procedures.
In the world of sports, data science and analytics are leading to game-changing insights. This book provides a solid understanding of these new trends and offers the interested reader a starting point from which to dive into this blossoming field.
This book covers Monté Carlo Methods and computer simulation for applications like calculating Pi, integration, areas, and volumes. It also introduces the novel Complex Probability Paradigm. For scholars and students in mathematics, computer science, and science in general.
Master the core methods of statistical research. This practical guide covers key techniques, from sampling to interconnection analysis, teaching you how to apply them effectively and interpret your results with confidence.
This book analyzes algorithms for stochastic phenomena like random processes and Markov chains. It presents a novel “Complex Probability Paradigm” applied to Brownian motion, making it ideal for researchers and students in mathematics, computer science, and science.
Explore the revolutionary work of David Hilbert, a giant of 20th-century mathematics. This book examines his axiomatic method, his famous 23 Problems, his program for the foundations of mathematics, and his crucial contributions to relativity and quantum physics.
The beauty of game theory is its application to real world problems. This book commemorates the marriage of the theory and practice, not in heaven, but in the real world.
This book introduces Kolmogorov-Zurbenko Filters, powerful data analysis tools that discover phenomena hidden within data. Rather than forcing models, these methods obtain accurate results in noisy environments by separating sources of influence to explain the total picture.
This book presents the direct integration method, a tool for analyzing the elastic response of nonhomogeneous solids to thermal and force loadings. By reducing elasticity problems to integral equations, this method provides efficient, closed-form solutions for these materials.
This book provides insights into the dynamics of control systems, highlighting the first-time use of Morse theory to describe global dynamics. It is an accessible source of definitions, examples, theorems, and open questions for students, researchers, and professionals.
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