Last modified: 31 May 2022 13:05
This course will introduce machine learning, artificial intelligence and forecasting with applications in finance and management.
The course will explore recent trends in FINTECH, which are based on data analytics and recent advances in machine learning. The course will provide an introduction to Python, which has become the dominant general-purpose programming language in data analytics and machine learning. To be clear, students are not expected to have any prior knowledge of Python or any other programming language.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Third Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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The course is structured into ten units and associated tutorials.
Lecture 1: Introduction
Big data, machine learning, artificial intelligence (AI) and its applications in finance and management.
Lecture 2: Getting started with Python
Algorithmic thinking, installation, Anaconda, objects, lists, tuples and arrays, strings
Lecture 3: Structure, control & data
Loops, while / if, array operations, branching, nesting, data import, visualisation of data
Tutorial 1: Analysing data
Data input, importing data, plotting data, descriptive analysis
Lecture 4: What is machine learning?
Types of machine learning, artificial neurons, perception learning algorithm
Lecture 5: Machine learning classifiers
Introduction to scikit-learn, logistic regression, maximum margin, non-linear problems
Tutorial 2: Simple algorithms
Design of a good algorithm, data processing, predictions
Lecture 6: Dimensionality reduction
Principal component analysis (PCA), supervised and unsupervised learning, nonlinear mappings
Lecture 7: Artificial Intelligence
Definitions of AI, building a neural network in Python
Tutorial 3: Time series analysis
Explore financial time series, descriptive analysis in Python, non-stationary time series
Lecture 8: Fintech
Big data in finance, peer-to-peer lending, online / mobile banking, cryptos
Lecture 9: Forecasting
In-sample versus out-of-sample forecasting, comparison of forecasting tools
Tutorial 4: Advanced tools in Python
Advanced codes, projection of new data points, applications
Lecture 10: The future of machine learning and AI
What have we learned about machine learning? What is AI? What are the consequences?
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Feedback will be provided within 3 weeks of submission. |
Word Count | 1500 | |
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Assessment Type | Summative | Weighting | 75 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Feedback will be provided within three weeks of submission. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Feedback is provided within 3 weeks of submission. |
Word Count | 3000 | |
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Evaluate | By the end of this course students shall critically evaluate the processes and practices of machine learning and artificial intelligence. |
| Conceptual | Understand | Understand the role of big data and its applications in finance and management. |
| Reflection | Create | Develop programming skills in Python to analyse data and create machine learning code. |
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