000			02699nam a22002417a 4500
999			_c4018 _d4018
005			20221123145336.0
008			221123b ||||| |||| 00| 0 eng d
020			_a9781786349644
082			_a332.0285631 _bNI
100			_aNi, Hao _99228
245			_aAn introduction to machine learning in quantitative finance: _badvanced textbooks in mathematics
260			_bWorld Scientific _aNew Jersey _c2021
300			_axxiv, 238 p.
365			_aUSD _b48.00
504			_aContents: 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
520			_aIn 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!
650			_aMachine learning _92343
650			_aFinance--Mathematical models _9180
700			_aDong, Xin _910330
700			_aZheng, Jinsong _910331
700			_aYu, Guangxi _910332
942			_2ddc _cBK