Machine Learning for Financial Risk Management with Python

The information systems security (InfoSec) profession remains one of the fastest growing professions in the world today. With the advent of the Internet and its use as a method of conducting business,

A valuable reference for understanding operational risk Operational Risk with Excel and VBA is a practical guide that only discusses statistical methods that have been shown to work in an operational

International Financial Management provides students with a foundation for analysis through a text that is well-organized, comprehensive, and provides up-to-date coverage of the topics. Like the first nine editions, it is written based on two tenets: emphasis on the basics and emphasis on a managerial perspective.The scope and content of international finance has been fast evolving due to cycles of deregulations and regulations of financial markets, product innovations, and technological advancements. As capital markets of the world are becoming more integrated, a solid understanding of international finance has become essential for astute corporate decision making. Reflecting the growing importance of international finance as a discipline, we have seen a sharp increase in the demand for experts in the area in both the corporate and academic worlds.International Financial Management discussion is written so that a self-contained treatment of each subject is presented in a user-friendly

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif