Last modified: 22 Jul 2026 13:42
This course introduces machine learning and forecasting with applications in finance. It will explore recent trends in financial technology (FinTech) from academic and industry perspectives, which are based on data analytics and recent advances in machine learning. This course keeps the minimum of math-related content and focuses more on the application perspective. The course is based on Python, the gold-standard programming language for data analytics and machine learning.
| Study Type | Undergraduate | Level | 4 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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This comprehensive course is designed to integrate machine learning with financial applications, using the programming language Python as a tool for data analysis and model implementation. Students will explore the burgeoning field of financial data science, gaining hands-on experience with real-world datasets to perform tasks ranging from portfolio management to algorithmic trading. With a focus on both the academic and practical aspects of machine learning in finance, the course will prepare students for the challenges and opportunities in this dynamic sector.
The course may include, but may not be limited to:
1: Introduction to Machine Learning in Finance
Objective: To provide a foundational understanding of machine learning (ML) within the context of finance, detailing historical developments, current challenges, and the various applications of ML both in academic research and the financial industry.
2: Financial Data Science with Python
Objective: To introduce and review the structure of typical financial data, utilising Python for data plotting and manipulation. Students will familiarise themselves with essential Python packages.
3: Portfolio Management with Python
Objective: To introduce concepts of portfolio mean, standard deviation, and optimisation, and to calculate risk-adjusted returns using Python.
4: Machine Learning Basics
Objective: To discuss the fundamentals of machine learning, including various types, algorithms, and workflows.
5: Supervised Learning-Regression
Objective: To delve into supervised learning with a focus on regression, comparing linear and non-linear model approaches for regression tasks.
6: Supervised Learning - Classification
Objective: To explore linear and non-linear models used in classification tasks within supervised learning frameworks.
7: Unsupervised Learning
Objective: To study unsupervised learning techniques such as K-means clustering and PCA, and to understand their integration with supervised learning models.
8: Model Selection with Grid Search
Objective: To introduce advanced techniques in hyperparameter tuning and model selection, with a specific focus on grid search methodology.
9: Textual Analysis
Objective: To provide an overview of text analysis methods including bag-of-words, n-gram, and TF-IDF, and to discuss their financial applications with API access.
10: Algorithm Trading
Objective: To introduce algorithmic trading, exploring various strategies and their implementations including automated trading.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 75 | |
|---|---|---|---|---|
| Assessment Weeks | 39 | Feedback Weeks | 42 | |
| Feedback |
Written feedback will be provided to each student. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Students should grasp the integration of machine learning in finance and gain skills in manipulating and analysing financial data using Python. |
| Procedural | Apply | Students should acquire skills to build, evaluate, and apply machine learning models—such as regression and classification—to real-world financial scenarios like portfolio management. |
| Procedural | Apply | ILO Description (maximum 200 characters) Students are expected to achieve expertise in the practical applications of machine learning in finance, including performing textual analysis and implementin |
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | 39 | Feedback Weeks | 42 | |
| Feedback |
Written feedback will be provided to each group. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Students should grasp the integration of machine learning in finance and gain skills in manipulating and analysing financial data using Python. |
| Procedural | Apply | Students should acquire skills to build, evaluate, and apply machine learning models—such as regression and classification—to real-world financial scenarios like portfolio management. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | 50,51 | Feedback Weeks | ||
| Feedback |
Written feedback will be provided to each student. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Apply | Students should acquire skills to build, evaluate, and apply machine learning models—such as regression and classification—to real-world financial scenarios like portfolio management. |
| Procedural | Apply | ILO Description (maximum 200 characters) Students are expected to achieve expertise in the practical applications of machine learning in finance, including performing textual analysis and implementin |
| Conceptual | Understand | Students should grasp the integration of machine learning in finance and gain skills in manipulating and analysing financial data using Python. |
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