Postgraduate Course: Advanced Machine Learning (CMSE11711)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Machine learning is centered on the development and application of algorithms that learn and adapt based on data. This course covers both theoretical concepts necessary for making informed choices about models and more advanced algorithms not covered in introductory courses. The intended audience are students seeking a deeper technical understanding of the field. |
| Course description |
With machine learning experiencing a continuously increasing interest across research and industry, the application of these developments comes with risks if their properties and limitations are not sufficiently considered. This course addresses these risks by providing the technical knowledge necessary to choose the right tool for the task, covering a range of modern machine learning models and related concepts with wide-ranging applicability.
To this end, this course enables students to explain, assess and justify these methods, and to develop an awareness of how to address ethical concerns. The course consists of a series of in-person lectures targeting specific areas of interest in machine learning with a focus on theoretical aspects, as well as hands-on workshops. Both a working knowledge of mathematical concepts and some programming experience, for example from prior courses, are assumed.
Outline Content:
The set of topics covered can vary slightly from year to year, but will include many of the following:
- Algorithm design and complexity theory
- Predictive ensembles and model selection
- Clustering methods and spatio-temporal analysis
- Convolutional neural networks and image analysis
- Autoencoders and generative adversarial networks
- Recurrent neural networks and time series analysis
- Algorithmic bias and fairness in machine learning
- Parallel computing and hardware acceleration
Student Learning Experience:
Weekly lectures and five hands-on workshops enabling students to apply methods covered in lectures.
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Course Delivery Information
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| Academic year 2026/27, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 20,
Seminar/Tutorial Hours 5,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
171 )
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| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
30% Project report (Group) - 3,000 words - Assesses course Learning Outcomes 1,2,3
70% Written Exam (Individual) - 2 hours - Assesses course Learning Outcomes 1,2,4 |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on the assessments within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Explain the functionality and properties of modern machine learning methods
- Critically compare and evaluate the suitability of methods for given problems
- Apply covered implementations and justify the respective model selection
- Demonstrate knowledge of limitations and relevant ethical concerns
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Reading List
Core text(s)
Bishop, C. M. (2016), Pattern Recognition and Machine Learning. New York : Springer, ISBN: 9781493938438
Goodfellow, I.; Bengio, Y.; Courville, A. (2016), Deep Learning, Cambridge, MA: MIT Press. Online: https://www.deeplearningbook.org |
Additional Information
| Graduate Attributes and Skills |
Practice: Applied Knowledge, Skills and Understanding
After completing this course, students should be able to:
Work with a variety of organisations, their stakeholders, and the communities they serve - learning from them, and aiding them to achieve responsible, sustainable and enterprising solutions to complex problems.
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to quality.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly. |
| Keywords | Machine learning,Deep learning,Algorithms |
Contacts
| Course organiser | Dr Ben Moews
Tel: (01316) 508074
Email: Ben.Moews@ed.ac.uk |
Course secretary | |
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