Overview
Introduction to Machine Learning in Quantitative Finance is a book by Hao Ni, Guangxi Yu, Jinsong Zheng and Xin Dong, first published in 2021. In this catalogue it is classified mainly under Quantitative Finance, with secondary coverage of Financial Machine Learning.
Audience and depth. The rule-based model places its mathematical difficulty at intermediate level (6/10); readers are typically expected to know calculus, linear algebra, machine learning, probability theory, statistics and stochastic processes.
Orientation. The theory-versus-practice balance could not be determined from metadata. The archive holds 6 papers whose topics and vocabulary overlap with this book; those links are inferred (see the research connections below).
How to read this page. Topics, difficulty, prerequisites and orientation are model-derived (rule-based, rule_based_v1) from bibliographic metadata such as title, subject headings and edition data. They were not produced by reading the book and are not a review. Relevance confidence is 86% and difficulty confidence is 78%; the catalogue lists no ratings or awards.