Machine Learning in Finance (FNCE30014)
Undergraduate level 3Points: 12.5On Campus (Parkville)
About this subject
Contact information
Semester 2
Overview
Availability | Semester 2 |
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Fees | Look up fees |
Machine learning has been revolutionizing the financial industry, offering the potential to disrupt traditional structures and practices. This subject is meticulously organized around several real-world issues to introduce fundamental economic and financial problems and demonstrate how machine learning can provide transformative solutions to these problems. Throughout the course, case studies will be used to illustrate these core concepts, allowing students to see the practical application of theoretical knowledge. The emphasis is on building a strong foundation in machine learning techniques and their financial applications, ensuring that students can confidently apply these concepts in novel situations they may encounter in their professional careers.
Additionally, students will engage in hands-on projects and exercises that reinforce the material covered in lectures. This practical approach ensures that they not only understand the theoretical underpinnings but also gain the skills needed to implement machine learning solutions in real-world financial contexts.
By the end of the course, students will have a comprehensive understanding of how machine learning can address various economic and financial challenges, preparing them to drive innovation and improvement in the financial industry.
Intended learning outcomes
On completion of this subject, students should be able to:
- identify fundamental economic and financial real‐world problems in the society;
- determine how major advances in artificial intelligence and machine learning are applicable to finance problems;
- demonstrate basic knowledge of techniques in artificial intelligence and machine learning;
- distinguish disruption from mere computerisation;
- acquire skills to solve practical financial problems using Machine Learning;
- learn to think outside the box;
- critically evaluate new ideas and their implementation;
- recognise opportunities to innovate.
Generic skills
On completion of this subject students should be able to:
- high level of development: problem solving; critical thinking; interpretation and analysis;
- moderate level of development: statistical analysis; algorithm development;
- some level of development: programming.
Last updated: 4 March 2025