Random Matrix Methods For Machine Learning
Download Random Matrix Methods For Machine Learning PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Random Matrix Methods For Machine Learning book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Random Matrix Methods for Machine Learning
Author: Romain Couillet
language: en
Publisher: Cambridge University Press
Release Date: 2022-07-21
This unified random matrix approach to large-dimensional machine learning covers applications from power detection to deep neural networks.
Random Matrix Methods for Machine Learning
Author: Romain Couillet
language: en
Publisher: Cambridge University Press
Release Date: 2022-07-21
This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging from SVMs, through semi-supervised learning, unsupervised spectral clustering, and graph methods, to neural networks and deep learning. For each application, the authors discuss small- versus large-dimensional intuitions of the problem, followed by a systematic random matrix analysis of the resulting performance and possible improvements. All concepts, applications, and variations are illustrated numerically on synthetic as well as real-world data, with MATLAB and Python code provided on the accompanying website.
Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
In today's fast-paced financial landscape, professionals face an uphill battle in effectively integrating data analytics and artificial intelligence (AI) into quantitative risk assessment and financial computation. The constantly increasing volume, velocity, and variety of data generated by digital transactions, market exchanges, and social media platforms offer unparalleled financial analysis and decision-making opportunities. However, professionals need sophisticated AI technologies and data analytics methodologies to harness this data for predictive modeling, risk assessment, and algorithmic trading. Navigating this complex terrain can be daunting, and a comprehensive guide that bridges theory and practice is necessary. Data Analytics and AI for Quantitative Risk Assessment and Financial Computation is an all-encompassing reference for finance professionals, risk managers, data scientists, and students seeking to leverage the transformative power of AI and data analytics in finance. The book encapsulates this integration's theoretical underpinnings, practical applications, challenges, and future directions, empowering readers to enhance their analytical capabilities, make informed decisions, and stay ahead in the competitive financial landscape.