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Numéro d'article:
43527749
Machine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition
Numéro d'article:
43527749
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Ce qui se démarque
Comprehensive Insights
Provides in-depth analysis of machine learning applications in finance, helping professionals understand complex concepts and implement strategies effectively.
Practical Case Studies
Includes real-world examples and case studies that bridge the gap between theory and practice, enhancing learning for finance practitioners.
Expert Contributions
Features insights from industry experts, offering valuable perspectives and practical advice that enrich the reader's understanding of finance and technology.
Détails du produit
- Introduces machine learning methods in finance with a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance
- Emphasizes the choice of algorithm for financial data modeling and decision making using theory and hypothesis tests
- Target audience includes advanced graduate students, academics, quants, and data scientists in financial econometrics, mathematical finance, applied statistics, and quantitative finance
- Divided into three parts covering theory and applications, with particular focus on supervised learning, neural networks, deep learning, Gaussian processes, time series data, and reinforcement learning in finance
- Provides Python code examples and more than 80 mathematical and programming exercises with worked solutions available to instructors
- Includes a final chapter that presents the frontiers of machine learning in finance from a researcher's perspective
| Publisher | Springer |
| Publication date | July 2, 2020 |
| Edition | 1st ed. 2020 |
| Language | English |
| Print length | 573 pages |
| ISBN-10 | 3030410676 |
| ISBN-13 | 978-3030410674 |
| Item Weight | 2.05 pounds (930 grams) |
| Dimensions | 6.14 x 1.25 x 9.21 inches (15.6 x 3.2 x 23.4 cm) |
À qui est-ce destiné ?
-
Finance Professionals
Ideal for finance professionals seeking to integrate machine learning techniques into their decision-making processes to enhance efficiency.
-
Data Scientists
Data scientists looking to apply machine learning methodologies specifically within the finance sector will find valuable insights and applications.
-
Students and Academics
Students and academics studying finance or data science can use this book as a comprehensive resource for theoretical and practical applications.
-
Beginners in Finance
Individuals with no background in finance may struggle to grasp the concepts presented due to complexity and terminology.
DESCRIPTION DU PRODUIT
Machine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition
About This Item
Looking to dive deeper into the world of finance and machine learning? Look no further than Machine Learning in Finance: From Theory to Practice. This comprehensive hardcover book delves into the applications of artificial intelligence and machine learning techniques in the finance industry, offering a practical look at predictive modeling and financial data analysis. Written by industry experts, this book is perfect for finance professionals looking to explore the world of machine learning and its potential applications in their field.
Discover the latest finance machine learning algorithms and techniques, including the use of robo-advisors and predictive modeling for financial forecasting and trading. Whether you're just beginning to explore the intersection of finance and machine learning or you're a seasoned expert in the field, Machine Learning in Finance: From Theory to Practice is an indispensable resource that belongs on every finance professional's bookshelf. Get your hardcover copy today and take your knowledge of finance and machine learning to the next level!.
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Product Buying Guide
Machine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition is an essential guide that introduces machine learning methods in the finance industry. It presents a unified treatment of machine learning, financial econometrics, and quantitative finance, emphasizing how theory and hypothesis tests inform the choice of algorithms for financial data modeling and decision making. This book is designed for advanced graduate students, academics, quants, and data scientists in the field of quantitative finance.
Product Specifications
- Machine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition
- 1st
- 2020
- Advanced graduate students, academics, quants, and data scientists
- Hardcover
- Approximately 400 pages
- English
- Python code examples provided
Key Features
- Presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance
- Covers supervised learning for both cross-sectional and time series data, including Bayesian and frequentist perspectives
- Puts firm emphasis on neural networks, deep learning, Gaussian processes, and reinforcement learning
- Includes examples in investment management, derivative modeling, trading, stochastic volatility, fixed income modeling, and wealth management
- Provides over 80 mathematical and programming exercises with worked solutions available to instructors
Usage Scenarios
- Ideal for academics and researchers in financial econometrics, mathematical finance, and applied statistics
- Suitable for quants and data scientists looking to advance their skills in quantitative finance
- Applicable for individuals involved in investment management, trading, derivative modeling, and wealth management
Usage Scenarios
- Introduction to Machine Learning for Financial Prediction by Hardcover – May 28, 2013
- Advances in Financial Machine Learning by Marcos Lopez de Prado – February 21, 2018
Some User Review
- This book provided a comprehensive understanding of machine learning methods in finance and their practical applications. The inclusion of Python code examples was particularly helpful for reinforcing the concepts.
- As an academic, I found this book to be an invaluable resource for bridging the gap between theory and practice in the field of quantitative finance. The exercises and worked solutions further enhanced the learning experience.
Competitors
- Market research indicates that the price of this book aligns with similar publications in the field of machine learning and quantitative finance.
Buying Considerations
- Consider the intended audience, which primarily includes advanced graduate students, academics, quants, and data scientists.
- Verify that the language of the book aligns with the reader's proficiency in English, as it is exclusively available in English.
- Assess the suitability of the hardcover format for the reader's preference and long-term usage.
Conclusion
Machine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition is a valuable resource that seamlessly integrates machine learning with quantitative finance. It is well-suited for advanced graduate students, academics, quants, and data scientists seeking to deepen their understanding and practical applications in this emergent field.
Voir moinsMachine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition is an essential guide that introduces machine learning methods in the finance industry. It presents a unified treatment of machine learning, financial econometrics, and quantitative finance, emphasizing how theory and hypothesis tests inform the choice of algorithms for financial data modeling and decision making. This book is designed for advanced graduate students, academics, quants, and data scientists in the field of quantitative finance. Continue Reading
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Probability & Statistics Editorial Review
The book "Machine Learning in Finance: From Theory to Practice 1st ed. 2020 Edition" cuts through the hype and provides a discussion on machine learning in finance that is relevant to professionals. The book grounds its discussion in concepts that will be familiar to quants, and has well-designed Python notebooks that present examples of the analysis. The insights on inverse reinforcement learning presented in the book are particularly interesting. The book includes multiple choice questions, exercises, and an extra instructor booklet available to assist in the classroom. The authors demonstrate that the "black boxes" of machine learning are actually fairly intuitive and implementable. Overall, this book is an amazing and comprehensive presentation of many different relevant and useful concepts.
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Avantages
- Relevant to professionals
- Grounded in familiar concepts
- Multiple choice questions, exercises, and instructor booklet available
- Well-designed Python notebooks
- Fairly intuitive and implementable concepts
Les inconvénients
- First edition is a bit less polished than some may like
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