In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git

Seismic Risk Analysis of Nuclear Power Plants addresses the needs of graduate students in engineering, practicing engineers in industry, and regulators in government agencies, presenting the entire process of seismic risk analysis in a clear, logical, and concise manner. It offers a systematic and comprehensive introduction to seismic risk analysis of critical engineering structures focusing on nuclear power plants, with a balance between theory and applications, and includes the latest advances in research. It is suitable as a graduate-level textbook, for self-study, or as a reference book. Various aspects of seismic risk analysis - from seismic hazard, demand, and fragility analyses to seismic risk quantification, are discussed, with detailed step-by-step analysis of specific engineering examples. It presents a wide range of topics essential for understanding and performing seismic risk analysis, including engineering seismology, probability theory and random processes, digital signal processing, structural dynamics, random vibration, and engineering risk and reliability.

In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!The Python codes contained within An Introduction to Machine Learning in Quantitative Finance have been made publicly available on the author's GitHub: https://github.com/deepintomlf/mlfbook.git