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An Introduction to Machine Learning in Quantitative Finance

Discover how machine learning can be used to solve financial data problems and create informative insights and predictions.

A young male computer programmer looking at his computer with overlay abstract finance economic graphs in the foreground.

Explore the applications of machine learning for quantitative finance

Over the past few years, machine learning (ML) has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.

This four-week course from University College London will demystify machine learning by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data.

Learn to use supervised learning models such as linear regression

Supervised learning is a category of machine learning that uses algorithms to classify data and create predictions.

You’ll be provided with an overview of supervised learning, as well as linear and non-linear regression with regularisation and classification. This will enable you to learn other new supervised learning algorithms in a systematic manner.

Understand how to use deep learning for predictive analytics in finance

Huge datasets are incredibly common in the financial sector, and present a significant challenge to researchers and analysts.

On this course, you’ll familiarise yourself with neural networks and understand how deep learning can be used to analyse large datasets and create accurate financial predictions. At the end of the course, you’ll put your learning into practice by tackling an empirical financial data problem using machine learning end-to-end.

Study with the experts at University College London

Your course educators are faculty members of the financial mathematics group at the UCL and Shanghai University.

With the help of their extensive research and experience, you’ll be empowered to solve real-world financial challenges through the application of modern machine learning methods.

Syllabus

  • Week 1

    Introduction to the course

    • Welcome to the course

      Welcome to the course! Meet our team and your fellow learners.

    • What is Machine Learning?

      We give a brief introduction to Machine Learning, including its main types and examples.

    • Quantitative Finance

      We will identify the challenges and promises of ML in Quantitative Finance.

    • Next generation of talents in Quantitative Finance

      The impact of ML has gradually changed the landscape of quantitative finance. You will learn about the skillsets required for the next generation of quantitative researchers to adapt to the future financial world.

    • End of Week 1

      After summarizing the overview of ML in quantitative finance, it is time for exercise. Let us set up the Python programming environment for your homework.

  • Week 2

    Supervised Learning in financial applications

    • Welcome to Week 2

      Learn about what tasks can be solved by supervised learning, and the main categories of supervised learning (regression v.s. classification).

    • Linear Regression

      We introduce the most basic supervised learning method, i.e., linear regression.

    • Regularization

      Learn about the overfitting issue and the regularization method to tackle this problem.

    • Classification

      In this activity, we introduce classification, which is a supervised learning task with categorical output. You learn how to extend the regression framework to tackle classification tasks.

    • End of Week 2

      We summarize a general supervised learning framework and the linear regression with regularization covered in Week 2.

  • Week 3

    Learning derivative pricing via Deep Neural Networks

    • Introduction to Week 3

      Learn about a brief introduction to deep neural networks and the underlying reason why they are powerful and universal models for data of different kinds.

    • Deep Neural Networks (DNNs)

      You will learn the model architecture of artificial neural networks, including shallow neural networks and deep neural networks (DNNs).

    • Training DNNs

      Introduce the back-propagation algorithm for optimizing the model parameters of Deep Neural Networks.

    • Applications of DNNs to derivative pricing

      Apply DNNs to learn the derivative pricing from data using Python.

    • End of Week 3

      Summarize the takeaway messages of DNNs.

  • Week 4

    Limit order book prediction via Recurrent Neural Networks

    • Introduction to Week 4

      We provide a brief introduction to Recurrent Neural Networks (RNNs), highlighting their strength in modelling time series data.

    • Recurrent Neural Networks

      Find out the mathematical formulation of Recurrent Neural Network (RNN) and how to train the RNN models.

    • Limit order book prediction

      We showcase how to apply RNN-based models for the tasks of limit order book prediction.

    • End and next step

      We give a wrap-up of the course. This is the end activity of the course. After passing the final test, you will earn a Certificate of Achievement on this course.

When would you like to start?

Start straight away and join a global classroom of learners. If the course hasn’t started yet you’ll see the future date listed below.

Learning on this course

On every step of the course you can meet other learners, share your ideas and join in with active discussions in the comments.

What will you achieve?

By the end of the course, you‘ll be able to...

  • Describe a high-level picture of machine learning techniques in quantitative finance.
  • Identify the main categories of machine learning tasks, i.e. supervised learning, unsupervised learning and reinforcement learning.
  • Apply a general framework of supervised learning to acquire new supervised learning algorithms in a systematic manner.
  • Describe the mathematics foundation of linear regression with/without regularization and neural networks.
  • Apply linear regression and neural networks models to solve real-world financial data problems.

Who is the course for?

This course is designed for anyone interested in machine learning and quantitative finance with a basic background in probability and Python programming.

It will be of particular interest to final-year undergraduate students or MSc students in financial mathematics or related subjects, pursuing a career in quantitative finance or data science.

It will also be suited to practitioners in quantitative finance.

What software or tools do you need?

Python

Who will you learn with?

Hao Ni is a Professor of Mathematics at University College London.

I am currently Lecturer at the Department of Mathematics at UCL . I am Fellow of the Higher Education Academy since 2018.

For more visit camilogarciatrillos.com

Alex Tse is a lecturer at the Financial Mathematics group of University College London.

Weiguan is a lecturer in Finance at Shanghai University. He obtained a Ph.D. in Mathematics from the London School of Economics.

Who developed the course?

UCL (University College London)

UCL was founded in 1826. It was the first English university established after Oxford and Cambridge, and the first to open up university education to those previously excluded from it.

What's included?

This is a premium course. These courses are designed for professionals from specific industries looking to learn with a smaller group of like-minded individuals.

  • Unlimited access to this course
  • Includes any articles, videos, peer reviews and quizzes
  • Tests to validate your learning
  • Certificate of Achievement to prove your success when you're eligible
  • Download and print your Certificate of Achievement anytime

Still want to know more? Check out our FAQs

Learning on FutureLearn

Your learning, your rules

  • Courses are split into weeks, activities, and steps to help you keep track of your learning
  • Learn through a mix of bite-sized videos, long- and short-form articles, audio, and practical activities
  • Stay motivated by using the Progress page to keep track of your step completion and assessment scores

Join a global classroom

  • Experience the power of social learning, and get inspired by an international network of learners
  • Share ideas with your peers and course educators on every step of the course
  • Join the conversation by reading, @ing, liking, bookmarking, and replying to comments from others

Map your progress

  • As you work through the course, use notifications and the Progress page to guide your learning
  • Whenever you’re ready, mark each step as complete, you’re in control
  • Complete 90% of course steps and all of the assessments to earn your certificate

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