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Machine Learning for Finance

You're reading from Machine Learning for Finance Principles and practice for financial insiders

Product type Paperback

Published in May 2019

Publisher Packt

ISBN-13 9781789136364

Length 456 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Jannes Klaas

James Le

View More author details

Table of Contents (15) Chapters

Machine Learning for Finance

Contributors

Preface

Other Books You May Enjoy

1. Neural Networks and Gradient-Based Optimization

2. Applying Machine Learning to Structured Data FREE CHAPTER

3. Utilizing Computer Vision

4. Understanding Time Series

5. Parsing Textual Data with Natural Language Processing

6. Using Generative Models

7. Reinforcement Learning for Financial Markets

8. Privacy, Debugging, and Launching Your Products

9. Fighting Bias

10. Bayesian Inference and Probabilistic Programming

Index

Chapter 10. Bayesian Inference and Probabilistic Programming

Mathematics is a big space of which humans so far have only charted a small amount. We know of countless areas in mathematics that we would like to visit, but that are not tractable computationally.

A prime reason Newtonian physics, as well as much of quantitative finance, is built around elegant but oversimplified models is that these models are easy to compute. For centuries, mathematicians have mapped small paths in the mathematical universe that they could travel down with a pen and paper. However, this all changed with the advent of modern high-performance computing. It unlocked the ability for us to explore wider spaces of mathematics and thus gain more accurate models.

In the final chapter of this book, you'll learn about the following:

The empirical derivation of the Bayes formula
How and why the Markov Chain Monte Carlo works
How to use PyMC3 for Bayesian inference and probabilistic programming
How various methods get applied...

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Authors (2)

James Le

James Le

James Le is currently a master's student studying Computer Science at Rochester Institute of Technology. He is pursuing a research path in Computer Vision using Deep Learning. Moreover, he is an active freelance data scientist and data journalist, with specialties including machine/deep learning, recommendation systems, and data analysis/visualization.

See other products by James Le

Jannes Klaas

Jannes Klaas

Jannes Klaas is a quantitative researcher with a background in economics and finance. He taught machine learning for finance as lead developer for machine learning at the Turing Society, Rotterdam. He has led machine learning bootcamps and worked with financial companies on data-driven applications and trading strategies. Jannes is currently a graduate student at Oxford University with active research interests including systemic risk and large-scale automated knowledge discovery.

See other products by Jannes Klaas

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