Machine Learning for Financial Risk Management with Python

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic

Many industry experts consider unsupervised learning the next AI frontier, one that may hold the key to general artificial intelligence. Armed with the conceptual knowledge in this book, data scientis

Essentials of Financial Management is paperback edition of an Open Access e-textbook suitable for students with limited knowledge of finance and financial markets. It answers the main questions of a c

Empowering you for success, Brigham/Ehrhardt's FINANCIAL MANAGEMENT: THEORY AND PRACTICE, 16th edition, equips you with a solid understanding of key theoretical concepts along with practical tools to

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.

The fourth edition of Essentials of Financial Management continues to provide students with a focused understanding of today’s corporate finance and financial management. This market-leading text offe

Disaster risk management is of increasing significance in today’s world. Every year, natural disasters cause tens of thousands of deaths and tens of billions of dollars’ worth of losses. Northeast Asi

Essential tools and guidance for effective nonprofit financial management Financial Management for Nonprofit Organizations provides students, professionals, and board members with a comprehensive refe

NOTE: Before purchasing, check with your instructor to ensure you select the correct ISBN. Several versions of the MyLab™and Mastering™ platforms exist for each title, and registrations ar

Use machine learning and Oracle Business Intelligence Enterprise Edition (OBIEE) as a comprehensive BI solution. This book follows a when-to, why-to, and how-to approach to explain the key steps invol

Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related.

Continuing the tradition established in prior editions, this product answers the key questions that any clinician encounters with a high-risk pregnancy: what are the risks for the woman and/or the baby with this condition? How do I manage a pregnancy complicated by this condition? How do I perform this procedure (e.g. amniocentesis, cesarean section)? All the chapters are newly written or updated to reflect current, evidence-based management and changes in practice. The 'Normal Values' section, a hugely popular reference source, is included. Over half of the chapters have new authors. New chapters have also been added to keep the content up to date with modern developments. The comprehensive online resource expands functionality by providing links to key websites (e.g. National Clinical Guidelines), video recordings - especially of procedures - and additional images. All online content will be reviewed annually and updated as necessary.

Hospital staff and caregivers are regularly exposed to biomechanical overload risk, particularly at spine and shoulder level a risk factor that will continue to rise with the progressive aging of the