Last modified: 6 Days, 21 Hours, 24 Minutes ago
This course introduces machine learning, artificial intelligence and financial data science in a financial context. Students will learn to access, process and analyse financial data using Python, evaluate key machine learning models, build basic neural networks, and explore related applications.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
|
||
Advances in Machine Learning in Finance introduces students to the rapidly growing role of data analytics, machine learning and artificial intelligence in modern financial practice. The course is designed for students who wish to understand how computational methods can be used to analyse financial data, support decision-making and develop technology-driven financial applications. Using Python as the main programming environment, students will learn how to access, clean, process and interpret financial data, and how to apply key machine learning techniques in a finance and business context.
The course covers both conceptual foundations and practical implementation. Students will explore Python basics, machine learning basics, supervised and unsupervised learning, classification methods, cross-validation, forecasting, textual analysis, web scraping, neural networks and selected applications in financial technology. The course also examines contemporary FinTech developments, including big data in finance, peer-to-peer lending, cryptocurrencies and algorithmic trading. Particular attention is given to the conditions under which machine learning models are appropriate, how their outputs should be interpreted, and how their limitations can be critically evaluated in financial decision-making.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 75 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Written feedback will be provided outlining whether and how students met the learning outcomes. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand the role of big data and its applications in finance and management. |
| Procedural | Evaluate | By the end of this course students shall critically evaluate the processes and practices of machine learning and artificial intelligence |
| Reflection | Create | Develop programming skills in Python to analyse data and create machine learning code. |
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Group presentation worth 25% of the course grade. Includes a 2,500-word report. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Written feedback will be provided outlining wether and how students met the learning outcomes. |
Word Count | 2000 | |
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Understand the role of big data and its applications in finance and management. |
| Procedural | Evaluate | By the end of this course students shall critically evaluate the processes and practices of machine learning and artificial intelligence |
| Reflection | Create | Develop programming skills in Python to analyse data and create machine learning code. |
We have detected that you are have compatibility mode enabled or are using an old version of Internet Explorer. You either need to switch off compatibility mode for this site or upgrade your browser.
