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MSc Applied AI for Public Services — an accredited Master's for people doing this work, not just reading about it.
Online or on campus
An intensive one-year master's for civil servants, NHS analysts and local-government officers who must commission, govern and evaluate AI systems in the public sector. Covers machine learning literacy, procurement, algorithmic accountability and service redesign. Delivered online (tuition-free) or on campus in Edinburgh. The University of Brigant teaches this award to one academic standard whether you study on campus in Edinburgh or on the tuition-free online pathway. Assessment is identity-checked; the marking scheme does not change with your postcode. This is an academic Master's award; where a field has a licensing or registration route, that remains a matter for your professional body or regulator. You will work through the material as a practitioner-researcher: applied methods, real or anonymised cases, and a supervised capstone or dissertation as the final component. Faculty mark whether you noticed the constraints a slide deck usually hides. MSc Applied AI for Public Services is taught as one award, not a list of unrelated modules. The published length is 12 months, and the credit total is 30. Students move from Mathematics & Probability for AI through Mathematics & Probability for AI; Machine Learning Theory & Practice; Programming Intelligent Systems; Deep Learning & Representation Learning; Natural Language Processing & Large Language Models; Computer Vision & Multimodal AI, and finish on Communicating AI to Non-Specialists. Each course has a question, a method, and a submission. In this field, students define a measurable question, fit a method that can be checked, and report where it fails. Reading is a dataset description, a methods note, and one published evaluation. The artefact a marker expects is a project note with the question, the method, the result, and the limitation. The award is built so that a graduate can do the following in practice: Build and evaluate machine-learning models against a real objective Judge where AI is and is not an appropriate solution, and say why Take a model from notebook to a monitored production service Teaching assumes the student can read a source, attempt a problem before the seminar, and revise after feedback. Attendance at live seminars is part of the design. The capstone or final course must use the methods of the earlier courses; a project that ignores them does not pass. The pages for each course name the topics that are examined. Those topics are the syllabus. A brochure line is not a substitute for them.
The programme carries 30 credits across four assessed modules. Delivery is tuition-free (£0) on the online pathway. Transcripts use letter grades and a GPA scale consistent with University of Brigant awards. No professional-body logo is used in marketing to imply endorsement.
Study online
Online tuition is free. Online examinations are free. There is no exam fee for online study.
On-campus study
Prefer to learn at our Edinburgh campus? Campus tuition and campus examinations are charged. The published campus price is on each programme page.
| Code | Course | Credits · hours |
|---|---|---|
| BX70101 | Mathematics & Probability for AI Mathematics & Probability for AI (BX70101) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Mathematics & Probability for AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| BX70102 | Machine Learning Theory & Practice Machine Learning Theory & Practice (BX70102) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Machine Learning Theory & Practice as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| BX70103 | Programming Intelligent Systems Programming Intelligent Systems (BX70103) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Programming Intelligent Systems as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| Code | Course | Credits · hours |
|---|---|---|
| BX70105 | Deep Learning & Representation Learning Deep Learning & Representation Learning (BX70105) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Deep Learning & Representation Learning as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| BX70106 | Natural Language Processing & Large Language Models Natural Language Processing & Large Language Models (BX70106) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Natural Language Processing & Large Language Models as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| BX70107 | Computer Vision & Multimodal AI Computer Vision & Multimodal AI (BX70107) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Computer Vision & Multimodal AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| Code | Course | Credits · hours |
|---|---|---|
| BX70109 | MLOps, Evaluation & Production AI MLOps, Evaluation & Production AI (BX70109) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using MLOps, Evaluation & Production AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| BX70110 | Responsible AI, Safety & Policy Responsible AI, Safety & Policy (BX70110) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Responsible AI, Safety & Policy as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| Code | Course | Credits · hours |
|---|---|---|
| BX70113 | Research Methods for Applied AI Research Methods for Applied AI (BX70113) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Research Methods for Applied AI as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
| BX70114 | Communicating AI to Non-Specialists Communicating AI to Non-Specialists (BX70114) is a 3-credit course on MSc Applied AI for Public Services, with 30 notional learning hours. By the end, students can define a measurable question, fit a method that can be checked, and report where it fails, using Communicating AI to Non-Specialists as the working context rather than a generic management example. The course is taught in three movements. First, students establish the terms and the decision the course is about. Second, they apply the method to a case, dataset, text, or design and compare it with a weaker alternative. Third, they revise the work after feedback and state what the conclusion cannot support. Preparation uses a dataset description, a methods note, and one published evaluation. Seminar time is for the decision, not for reading the materials aloud. Assessment is a project note with the question, the method, the result, and the limitation. A pass requires a clear method, evidence a marker can check, and an explicit limit. Credit is not awarded for summary alone.
| 3 · 30 h |
Modules are assessed through a published mix of coursework, applied projects, and examinations. Exam windows are announced in advance so students in other time zones are not forced into overnight sittings. Alternative arrangements are available where documented.
The published duration is 12 months. Teaching language: English (dual pathway: Edinburgh campus or tuition-free online; one academic standard). Actual time-to-complete depends on mode and any recognised prior learning.
You may study this award fully online from your country, or — where published — on campus at Brigant. Online study does not require a student visa. Campus study may.
Degree tuition for this award is published as £0 / tuition-free on the online pathway. Examination or administrative fees may apply at checkout — never an annual tuition invoice. Check the Fees page for any extras.
Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.
Assessment is typically a mix of coursework, projects, and examinations. Doctoral awards include a thesis or dissertation and an oral examination. Details sit in the programme specification and module outlines.
Recognition of the award for local employment, professional licence, or ministry attestation is decided by your employer or regulator. University of Brigant publishes verification pages for certificates. We do not claim automatic equivalence in every country.
Start an application on this website. Progress is saved from the first step. Admissions: admissions@brigant.uk.
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