INTRODUCTION TO MACHINE LEARNING AND QUANTITATIVE FINANCE

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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 authors Provide a systematic and rigorous introduction to supervised, unsupervised and reinforcement learning by establishing essential definitions and theorems. Dive into various types of neural networks, including artificial nets, convolutional nets, recurrent nets and recurrent reinforcement learning. Summarize key contents of each section in the tables as a cheat sheet. Include ample examples of financial applications. Showcase how to tackle an exemplar ML project on financial data end-to-end. Provide a GitHub repository https://github.com/deepintomlf/mlfbook.git that contains supplementary Python codes of all methods/examples. Featured 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! Sample Chapter(s) Preface Chapter 1: Overview of Machine Learning and Financial Applications Request Inspection Copy Contents: Preface About the Authors Acknowledgments Disclaimer Listings Overview of Machine Learning and Financial Applications Supervised Learning Linear Regression and Regularization Tree-based Models Neural Networks Cluster Analysis Principal Component Analysis Reinforcement Learning Case Study in Finance: Home Credit Default Risk Bibliography Index Readership: This textbook is suitable for MSc students or final year undergraduate students in financial mathematics, machine learning or computational finance. It would serve as a graduate textbook in introducing machine learning and its applications in quantitative finance. It may also be appropriate for those interested in pursuing a career in quantitative finance or for practitioners in the financial sector who wish to develop an in-depth understanding of machine learning and its applications to finance.

Gynecologic Health Care: With an Introduction to Prenatal and Postpartum Care (4版)

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Gynecologic Health Care: With an Introduction to Prenatal and Postpartum Care continues to set the standard for evidence-based gynecologic health care and well-being in an extensively updated fourth edition. As in prior editions, the text presents gynecologic health care using a holistic and person-centered approach. Encompassing both health promotion and management of gynecologic conditions, it provides clinicians and students with a strong foundation in gynecologic care and the knowledge necessary to apply it in clinical practice. With an emphasis on the importance of respecting the normalcy of physiology, it is an essential reference for all midwives, nurse practitioners, physician assistants, and other clinicians who provide gynecologic health care. Written by award-winning clinicians and educators, Gynecologic Health Care covers the topics clinicians and students need to know. Additional chapters provide an overview of prenatal and postpartum care, including anatomic and physiologic adaptations of normal pregnancy and common complications of pregnancy. The Fourth Edition features three new chapters: racism and health disparities, male sexual and reproductive health, and preconception care.