Undergraduate Course: Machine Learning Systems (UG) (INFR11280)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This course follows the delivery and assessment of Machine Learning Systems (INFR11269) exactly. For the academic year 2027/2027 Year 5 undergraduate students are able to register for INFR11269. |
| Course description |
This course follows the delivery and assessment of Machine Learning Systems (INFR11269) exactly. For the academic year 2027/2027 Year 5 undergraduate students are able to register for INFR11269.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Machine Learning Systems (INFR11269)
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Other requirements | Available only to those enrolled on Informatics CDT and MSc programmes, and UG5 students enrolled on MInf.
This course follows the delivery and assessment of Machine Learning Systems (INFR11269) exactly. For the academic year 2027/2027 Year 5 undergraduate students are able to register for INFR11269. |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- understand different types of data, queries, workflows, and architectures of machine learning systems. Demonstrate the appropriate choice and use of particular data structures, and architectures.
- construct, analyse and profile implementation to given machine learning systems and iteratively improve the performance of those systems.
- compare and evaluate different systems and suggest/synthesise an appropriate system adoption solution.
- present the system solutions and engage in professional dialogue with peers to improve their solutions.
- reflect on the wider quality and security issues of data and machine learning models when discussing with specialist practitioners.
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Reading List
| The course will be self-contained with no required book. |
Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/mls |
| Graduate Attributes and Skills |
Research and Inquiry:
This course aims to significantly enhance students' capabilities in solving real-world machine learning (ML) problems, with a strong emphasis on analytical and critical thinking skills directed at the performance and bottlenecks encountered in data-centric ML programs. Students will learn to manage ambiguity by accurately modelling data from real-world datasets and constructing effective queries. The competencies developed in both data handling and ML will foster an integrated approach to knowledge application when devising comprehensive coursework solutions.
Personal Effectiveness:
The coursework is structured into three stages, culminating in a requirement for students to submit a report that describes their programs, profiling results, strategies for addressing associated problems, thereby refining their planning and organizational skills. Creating a real-world ML system can broaden into wider business applications, potentially opening avenues for entrepreneurship.
Personal Responsibility:
We will instruct students on ensuring data security (e.g., using trusted hardware) and privacy (e.g., through federated learning), fostering ethical and socially responsible practices. Moreover, by developing their own ML programs, students will engage in independent learning. They will also be encouraged to go beyond standard solutions, which will inspire creative thinking.
Communication:
Students are required to submit a written report and will receive ongoing feedback from lecturers and tutors, enhancing their verbal and written communication skills. Working with datasets from various disciplines will also prepare them to operate effectively in cross-disciplinary environments. |
| Keywords | Computer Systems,Machine Learning,Data Management |
Contacts
| Course organiser | Dr Luo Mai
Tel:
Email: luo.mai@ed.ac.uk |
Course secretary | Mr Lachlan Boyd
Tel:
Email: lboyd@ed.ac.uk |
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