--- product_id: 182403800 title: "Machine Learning in Finance: From Theory to Practice" price: "VT27684" currency: VUV in_stock: true reviews_count: 13 url: https://www.desertcart.vu/products/182403800-machine-learning-in-finance-from-theory-to-practice store_origin: VU region: Vanuatu --- # Machine Learning in Finance: From Theory to Practice **Price:** VT27684 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** Machine Learning in Finance: From Theory to Practice - **How much does it cost?** VT27684 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.vu](https://www.desertcart.vu/products/182403800-machine-learning-in-finance-from-theory-to-practice) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance. Review: Best technical book on machine learning - The authors cut through the hype and rebranding that litters the field of machine learning. They discuss models that are relevant to finance. Most importantly for professionals, they ground their discussion in concepts that will be familiar to statisticians and numerical analysts (quants, in other words). Some of the work presented in this book is new, particularly the sections on inverse reinforcement learning. This will be an excellent resource for a graduate course. Those students who do not have the math background will likely be motivated to get it. There are well-designed Python notebooks that present examples of the analysis. Students with the ability to work with the concepts presented in this book would be welcome in any serious quant shop. Review: Great book. Congratulations to the authors! - An amazing and comprehensive presentation of many different relevant and useful concepts. The finance industry -- trading, asset management, risk management, banking, etc -- is most likely going to look much different in the not too distant future and much of this change is going to come from applications of this book's concepts. The authors also do a great job of demonstrating that these "black boxes" are actually not mysterious and overly complicated but rather fairly intuitive and implementable. If anyone has ever seen the movie "AlphaGO" and was wondering how that type of paradigm shift would apply to finance, the next step is to buy this book. ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #964,822 in Books ( See Top 100 in Books ) #222 in Business Statistics #480 in Statistics (Books) | | Customer Reviews | 4.6 out of 5 stars 116 Reviews | ## Images ![Machine Learning in Finance: From Theory to Practice - Image 1](https://m.media-amazon.com/images/I/61BkCx3ZdxL.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ Best technical book on machine learning *by A***G on January 7, 2021* The authors cut through the hype and rebranding that litters the field of machine learning. They discuss models that are relevant to finance. Most importantly for professionals, they ground their discussion in concepts that will be familiar to statisticians and numerical analysts (quants, in other words). Some of the work presented in this book is new, particularly the sections on inverse reinforcement learning. This will be an excellent resource for a graduate course. Those students who do not have the math background will likely be motivated to get it. There are well-designed Python notebooks that present examples of the analysis. Students with the ability to work with the concepts presented in this book would be welcome in any serious quant shop. ### ⭐⭐⭐⭐⭐ Great book. Congratulations to the authors! *by F***L on July 26, 2020* An amazing and comprehensive presentation of many different relevant and useful concepts. The finance industry -- trading, asset management, risk management, banking, etc -- is most likely going to look much different in the not too distant future and much of this change is going to come from applications of this book's concepts. The authors also do a great job of demonstrating that these "black boxes" are actually not mysterious and overly complicated but rather fairly intuitive and implementable. If anyone has ever seen the movie "AlphaGO" and was wondering how that type of paradigm shift would apply to finance, the next step is to buy this book. ### ⭐⭐⭐⭐⭐ The (new) standard texbook on machine learning in finance *by D***U on July 29, 2020* Brand new but I anticipate this will become THE textbook on the subject that many instructors will use to teach around the world. The book nicely builds up throughout the chapters. I find it great to include multiple choice questions, exercises and an extra instructor booklet available to assist in the classroom. And what a great idea to share Python code to make it all that more practical! I find the insights on inverse reinforcement learning particularly interesting. ## Frequently Bought Together - Machine Learning in Finance: From Theory to Practice - Advances in Financial Machine Learning - Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.vu/products/182403800-machine-learning-in-finance-from-theory-to-practice](https://www.desertcart.vu/products/182403800-machine-learning-in-finance-from-theory-to-practice) --- *Product available on Desertcart Vanuatu* *Store origin: VU* *Last updated: 2026-07-23*