Weekly study3 hours
An Introduction to Machine Learning in Quantitative Finance
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.
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?
Get a taste of this course
Find out what this course is like by previewing some of the course steps before you join:
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