Undergraduate Course: Advanced Topics in Machine Learning (UG) (INFR11289)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course follows the delivery and assessment of Advanced Topics in Machine Learning (INFR11286) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11286 instead. |
| Course description |
This course follows the delivery and assessment of Advanced Topics in Machine Learning (INFR11286) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11286 instead.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
This course requires practical mathematical application of algebra, vectors and matrices, calculus, probability, and problem solving. For example, topics may require you to differentiate linear algebra expressions with respect to vectors, interpret inner-products and quadratic forms geometrically, and compute expectations of linear algebra expressions under simple distributions. Some of the required details can be learned during the course, but some experience with most of these mathematical topics is required.
We assume that students have taken a basic introduction to machine learning that covers how to split and process data to apply, tune, and compare some baseline machine learning methods.
There are no assessed programming exercises in this course. However, we assume familiarity with programming and basic computer science (such as big-O complexity), so that we can consider questions about the practical implementation of methods. Students could be required to read short snippets of machine learning code using a popular framework (such as PyTorch or Jax in Python). |
Information for Visiting Students
| Pre-requisites | This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
This course requires practical mathematical application of algebra, vectors and matrices, calculus, probability, and problem solving. For example, topics may require you to differentiate linear algebra expressions with respect to vectors, interpret inner-products and quadratic forms geometrically, and compute expectations of linear algebra expressions under simple distributions. Some of the required details can be learned during the course, but some experience with most of these mathematical topics is required.
We assume that students have taken a basic introduction to machine learning that covers how to split and process data to apply, tune, and compare some baseline machine learning methods.
There are no assessed programming exercises in this course. However, we assume familiarity with programming and basic computer science (such as big-O complexity), so that we can consider questions about the practical implementation of methods. Students could be required to read short snippets of machine learning code using a popular framework (such as PyTorch or Jax in Python). |
Course Delivery Information
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| Academic year 2025/26, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 30,
Supervised Practical/Workshop/Studio Hours 10,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
154 )
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| Assessment (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% Examination |
| Feedback |
Targeted individual feedback will be provided on some of the formative work provided throughout the materials. Students are expected to engage with the course content in weekly workshop sessions with teaching assistants, and to discuss any unanswered questions on the class forum. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- identify how an aspect of an advanced machine learning topic applies to a given applied problem.
- derive mathematical details of machine learning methods in the topic area.
- critically compare and contrast alternative choices or variants of methods or approaches in the area.
- create accessible and useful explanations of the workings and failure modes of machine learning methods, including appropriate mathematical and implementation detail.
- identify the ethical and societal implications, including both benefits and risks, of the deployment of machine learning methods in the area.
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Reading List
| Reading material such as papers and lecture notes will be provided via the course website. |
Additional Information
| Graduate Attributes and Skills |
Apply critical analysis, evaluation and synthesis to current issues, or issues that are informed by forefront developments in the subject / discipline / sector.
Identify, conceptualise, and define new and abstract problems and issues.
Develop original and creative responses to problems and issues.
Critically review, consolidate and extend knowledge, skills, practices and thinking in subject / discipline / sector.
Deal with complex issues and make informed judgements in situations in the absence of complete or consistent data / information. |
| Keywords | ATML,Machine Learning,Geometric Learning,Gen AI,Representation Learning,Artificial Intelligenc |
Contacts
| Course organiser | Dr Rik Sarkar
Tel: (0131 6)50 4444
Email: Rik.Sarkar@ed.ac.uk |
Course secretary | Miss Toni Noble
Tel: (0131 6)50 2692
Email: Toni.noble@ed.ac.uk |
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