Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Financial Machine Learning
Top Free Algorithms Books 🌠 - 100% Free or Open Source!
  • Title: Financial Machine Learning
  • Author(s) Bryan T. Kelly, Dacheng Xiu
  • Publisher: University of Chicago (July, 2023)
  • Paperback: N/A
  • eBook: PDF
  • Language: English
  • ISBN-10: N/A
  • ISBN-13: N/A
  • Share This:  
`

Book Description

Survey the nascent literature on machine learning in the study of financial markets. Highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This book is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

About the Authors
  • N/A
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Data Science for Economics and Finance: Methodologies & Apps

    This book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance.

  • Art History and Machine Learning (Amanda Wasielewski)

    How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.

  • Physics-Based Deep Learning (Nils Thuerey, et al.)

    This book contains a practical and comprehensive introduction of everything related to deep learning in the context of physical simulations. All topics come with hands-on code examples in the form of Jupyter notebooks to quickly get started.

  • Probabilistic Machine Learning for Civil Engineers

    This comprehensive textbook presents basic machine learning methods for civil engineers who do not have a specialized background in statistics or in computer science. It includes several case studies that students and professionals will appreciate.

  • A Brief Introduction to Machine Learning for Engineers

    This book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference.

  • Machine Learning Engineering (Andriy Burkov)

    The most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. It embraces the most important thing you need to know about machine learning: mistakes are possible.

  • Machine Learning Yearning (Andrew Ng)

    In this book you will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

  • Machine Learning Engineering (Andriy Burkov)

    The most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. It embraces the most important thing you need to know about machine learning: mistakes are possible.

  • The Mechanics of Machine Learning (Terence Parr, et al)

    A primer on machine learning for programmers trying to get up to speed quickly. You'll learn how machine learning works and how to apply it in practice. Focus on just a few powerful models (algorithms) that are extremely effective on real problems,

  • Statistical Foundations of Actuarial Learning and its Applications

    This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future.

  • Introduction to Statistical Learning: with Applications in Python

    This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

Book Categories
:
Other Categories
Resources and Links