An introduction to machine learning in quantitative finance / Hao Ni (University College London, UK), Xin Dong (Citadel Securities LLC, UK), Jinsong Zheng (Huatai Securities, China), Guangxi Yu (SWS Research, China)

By:

Ni, Hao [VerfasserIn]

Contributor(s):

Resource type: Ressourcentyp: BuchBookLanguage: English Series: Advanced textbooks in mathematicsPublisher: New Jersey : World Scientific, [2021]Description: xxiv, 238 Seiten : IllustrationenISBN:

9781786349361
9781786349644

Subject(s):

Genre/Form:

Lehrbuch

Additional physical formats: 9781786349378 | 9781786349385 | Erscheint auch als: Introduction to machine learning in quantitative finance. Online-Ausgabe New Jersey : World Scientific, [2021] | Erscheint auch als: An introduction to machine learning in quantitative finance. Online-Ausgabe New Jersey : World Scientific, 2021. 1 Online-Ressource ( xxiv, 238 pages) | Erscheint auch als: An introduction to machine learning in quantitative finance. Online-Ausgabe New Jersey : World Scientific, 2021. 1 Online-Ressource (xxiv, 238 Seiten)DDC classification:

332.0285/631

LOC classification:

HG106

Summary: "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. Supplement Python codes of all the methods/examples in a GitHub repository. 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"PPN: PPN: 1737234750

Holdings ( 1 )

Holdings
Item type	Home library	Shelving location	Call number	Status	Date due	Barcode	Item holds
Freihandbestand ausleihbar	Fachbibliothek Mathematik	Bibliothek / frei aufgestellt	Fin./Vers. / Int	Available		36607672090

Total holds: 0