From
ZBK Books, Carlstadt, NJ, U.S.A.
Seller rating 5 out of 5 stars
AbeBooks Seller since March 10, 2023
Fast Shipping - Good and clean conditions used book. Pages and cover are intact. Limited notes marks and highlighting may be present. May show signs of normal shelf wear and bends on edges. Item may be missing CDs or access codes. May include library marks. Seller Inventory # ZWM.U3IS
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
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!
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
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.
About the Author:
Hao Ni is an associate professor in financial mathematics at University College London (UCL) and a Turing Fellow at the Alan Turing Institute since September 2016. Prior to this, she was a visiting postdoctoral researcher at ICERM and Department of Applied Mathematics at Brown University from September 2012 to May 2013. She continued her postdoctoral research at the Oxford-Man Institute of Quantitative Finance until 2016. She finished her D.Phil. in mathematics at the University of Oxford. Her research interests include stochastic analysis, financial mathematics and machine learning. More specifically, she is interested in non-parametric modelling effects of complex multi-modal data streams through rough paths theory and machine learning. Moreover, she has research interests on real-world applications, such as human-computer interface, computer vision and finance.
Xin Dong is currently a quantitative researcher working at Citadel Securities in London since 2018. She was a desk strategiest at Morgan Stanley in London from 2014 to 2018. Prior to that, she finished her PhD focused on point processes in the Department of Mathematics at Imperial College London in 2014. Her current research interests are on quantitative trading.
Jinsong Zheng is an algorithm engineer at Huatai Securities in China since 2019. Prior to this, he was a quantitative risk manager at Talanx AG in Germany. He got his D.Phil. in economics at the University of Duisburg Essen. He has extensive work experience in quantitative risk management and financial modelling. His current research interests are quantitative investment and asset allocation, especially using machine learning techniques and big data.
Guangxi Yu is a quantitative analyst at SWS Research in China. His current research includes financial data analysis, quantitative modelling and equity strategies. He is especially interested in machine learning applications in finance. Prior to this, he obtained his Master's degree in Financial Mathematics at University College London under the supervision of Dr Hao Ni.
Title: Introduction To Machine Learning In ...
Publisher: WSPC (EUROPE)
Publication Date: 2021
Binding: Soft cover
Condition: good
Seller: ZBK Books, Carlstadt, NJ, U.S.A.
Condition: good. Fast Shipping - Good and clean conditions used book. Pages and cover are intact. Limited notes marks and highlighting may be present. May show signs of normal shelf wear and bends on edges. Item may be missing CDs or access codes. May include library marks. Seller Inventory # ZWV.1786349647.G
Quantity: 1 available
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
Condition: New. Seller Inventory # ABLIING23Mar2912160172955
Quantity: Over 20 available
Seller: GreatBookPrices, Columbia, MD, U.S.A.
Condition: New. Seller Inventory # 42293022-n
Quantity: 1 available
Seller: Speedyhen, London, United Kingdom
Condition: NEW. Seller Inventory # NW9781786349644
Quantity: 3 available
Seller: BargainBookStores, Grand Rapids, MI, U.S.A.
Paperback or Softback. Condition: New. An Introduction to Machine Learning in Quantitative Finance 0.79. Book. Seller Inventory # BBS-9781786349644
Quantity: 5 available
Seller: Chiron Media, Wallingford, United Kingdom
Paperback. Condition: New. Seller Inventory # 6666-GRD-9781786349644
Quantity: 3 available
Seller: California Books, Miami, FL, U.S.A.
Condition: New. Seller Inventory # I-9781786349644
Quantity: Over 20 available
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New. Seller Inventory # 42293022-n
Quantity: Over 20 available
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # GB-9781786349644
Quantity: 3 available
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days. Seller Inventory # C9781786349644
Quantity: Over 20 available