BFF5555 · Financial machine learning

Overview

This unit explores the application of machine learning techniques in financial contexts, emphasising practical implementation using Python. Beginning with foundational concepts in statistical learning, the unit progresses through key supervised learning methods such as linear regression, classification, and tree-based models, before introducing unsupervised learning and deep learning techniques. Special attention is given to model evaluation, regularisation, and cross-validation. The unit culminates in advanced topics including natural language processing (NLP) in finance. Through interactive seminars and hands-on assessments, you will develop the skills to build, assess, and apply machine learning models to real-world financial problems.

Offerings

Assessment

The Handbook does not list a final examination among the assessment items. That is not a guarantee there is none.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. explain machine learning as a statistical learning paradigm and distinguish it from traditional statistical approaches
2. identify and describe key machine learning categories (supervised, unsupervised) and techniques, including their financial applications
3. implement machine learning models in Python, including regression, classification, tree-based methods, and deep learning, tailored to finance scenarios
4. evaluate model performance using techniques such as cross-validation and regularisation, and discuss challenges like overfitting

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.