Postgraduate Course: Machine Learning in Financial Services (INFR11284)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course replaces INFR11205 Introductory Applied Machine Learning (Semester 2) (IAML-PG2) from 2025/26.
This Semester 2 course is only available to students on the MSc in Finance Technology and Policy (within the Business School) and the MSc in Advanced Technology for Financial Computing.
This course introduces foundational concepts in applied machine learning and their applications in financial services and technology. Students will use current programming tools to solve realistic finance-related problems.
This is an introductory course teaching the foundations of machine learning. No prior machine learning knowledge is needed. |
| Course description |
The course will be delivered via in-person lectures. Online quizzes will be associated with lectured topics, intended for students to review their understanding. Lectures will involve other activities to aid learning. Lecture recordings will be available.
This course provides an in-depth exploration of machine learning methods and their applications in finance. Students will gain practical and theoretical skills in data analysis, algorithm implementation, and evaluation of models within the financial domain.
Content Outline:
- Introduction to Machine Learning in Finance: Supervised vs. unsupervised learning, practical applications in finance.
- Financial Data Representation: Categorical and real-valued attributes, feature extraction, basis expansion, financial index.
- Data Classification: Techniques such as Naive Bayes, logistic regression, decision trees, and neural networks. Applications in mortgage approval.
- Regression and Forecasting of financial trends: Linear regression, moving average, auto-regressive model, and autoregressive moving-average (ARMA). Applications in stock price prediction.
- Model Optimisation and Generalisation: Fitting models, generalisation, avoiding overfitting.
- Grouping and Unsupervised Learning: PCA, clustering, dimensionality reduction. Financial applications in grouping stocks and risk management.
- Evaluating Models: Accuracy, recall, and ROC curves.
- Ethical Considerations in Finance: Fairness, biases, and responsible use of machine learning. Applications in credit scoring.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Introductory Applied Machine Learning (INFD11005) OR
Machine Learning and Pattern Recognition (INFR11130) OR
Applied Machine Learning (INFR11211)
|
Other requirements | This Semester 2 course is only available to students on the MSc in Finance Technology and Policy (within the Business School) and the MSc in Advanced Technology for Financial Computing.
Students should check these maths and programming requirements carefully, as the course assumes and builds on these foundations. Experience has shown that students without this background can struggle with the course.
Maths requirements: 1. Linear algebra: Vectors: scalar (dot) product, transpose, unit vectors, vector length, orthogonality. Matrices: addition, matrix multiplication, matrix inversion. Eigenvectors, determinants quadratic forms. 2. Special functions: properties and combination rules for logarithm and exponential. 3. Calculus: Rules for differentiation of standard functions. Functions of several variables. Partial differentiation. Multivariate maxima and minima. 4. Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and n-dimensional generalizations. 5. Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions.
Programming requirements: Students should be familiar with programming in a modern object-oriented language, ideally Python which is the course language.
Finance requirements: Students should be familiar with financial market forecasting, credit scoring, fault detection, etc. |
Course Delivery Information
|
| Academic year 2025/26, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 28,
Seminar/Tutorial Hours 4,
Supervised Practical/Workshop/Studio Hours 4,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
158 )
|
| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam 70%
Coursework 30% |
| Feedback |
The course offers a robust and well-resourced framework to engage students with world-class Informatics teaching and research staff:
Use of Virtual Learning Environments (VLEs): Students will access resources via platforms such as Learn and other University-supported social platforms.
Interactive Course Forums: These forums enable students to pose questions to teaching staff and peers, fostering collaborative learning. The Piazza platform serves as the primary forum for course-related inquiries, covering topics such as lecture materials, labs, tutorials, and assignments. Students can register via the announcement link provided at the start of the course. Active participation is encouraged; answering peer questions not only reinforces understanding but also promotes collaborative learning. The platform is monitored regularly by the lecturer and teaching assistants to ensure timely responses.
Comprehensive Feedback Mechanisms: Tutorials, labs, and coursework integrate both peer and tutor feedback to support formative and summative learning.
For confidential concerns, students should email the course lecturer directly. However, whenever possible, using the forum is preferred as it enhances efficiency and benefits the entire class. |
| Exam Information |
| Exam Diet |
Paper Name |
Minutes |
|
| Main Exam Diet S2 (April/May) | Machine Learning in Financial Services (INFR11284) | 120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- explain the scope, goals, limitations, and mechanism of the covered machine learning methods, including logistic regression, tree models, and neural networks, and further explain how to select and implement appropriate machine learning models given a financial task.
- critically evaluate and compare the performance of various machine learning techniques covered in the syllabus in solving specific financial problems in a systematic way.
- apply the taught techniques to real-world practice to solve machine learning and finance problems, using appropriate software/open-access tools.
- analyse ethical implications of applying machine learning in critical financial areas, such as credit assessing and mortgage approval.
|
Reading List
Books that may be useful, but are not required:
- Probabilistic Machine Learning: An Introduction by Kevin P. Murphy (MIT Press 2022)
- Pattern Recognition and Machine Learning by Christopher Bishop (Springer 2007)
- Elements of Statistical Learning by T. Hastie, R. Tibshirani and J. Friedman (Springer 2009) |
Additional Information
| Graduate Attributes and Skills |
Research and Enquiry: Apply critical and analytical thinking to real-world data problems. Develop their problem-solving skills so they can better create, identify, and evaluate options in order to solve complex problems.
Personal Effectiveness: Developing capabilities in independent learning, time management, and flexibility during self-study before lectures.
Ethical Awareness, Personal Responsibility and Autonomy: Evaluate biases and ethical challenges in data-driven systems. Recognise and understand the ethical questions related to the application of machine learning algorithms
Communication: Effective collaboration and articulation of technical concepts. |
| Keywords | MLFS,ATFC,Machine Learning,Data Science,Finance,Informatics |
Contacts
| Course organiser | Dr Fengxiang He
Tel:
Email: fhe@ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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