Certificate

Graduate Certificate in AI for Managers

A focused, stackable credential that proves one capability fast.

Online or on campus

9 months15 creditsEnglish (dual pathway: Edinburgh campus or tuition-free online; one academic standard)

Overview

A focused credential for managers who must make decisions about AI without becoming engineers — capability and limits, use-case selection, procurement, risk, and change. Stackable toward the MSc Applied AI for Public Services. 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 graduate certificate 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. Graduate Certificate in AI for Managers is taught as one award, not a list of unrelated modules. The published length is 9 months, and the credit total is 15. Students move from Mathematics & Probability for AI through Mathematics & Probability for AI; Machine Learning Theory & Practice; Deep Learning & Representation Learning; MLOps, Evaluation & Production AI; Research Methods for Applied AI, and finish on Research Methods for Applied AI. 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 15 credits across a single stackable block. 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.

Who This Programme Is For

Software engineers and analysts moving into machine learning
Product managers who must own an ML-backed product
Public-sector and regulated-industry staff commissioning AI
Graduates in a quantitative discipline heading for research

How you can study

Recommended

Study online

100% tuition-free online

Online tuition is free. Online examinations are free. There is no exam fee for online study.

✦ Tuition: £0 / $0
✦ Examinations are free. There is no exam fee.
✦ Learn from anywhere · Live online seminars

Apply online →

On-campus study

Study at our campus

Prefer to learn at our Edinburgh campus? Campus tuition and campus examinations are charged. The published campus price is on each programme page.

✦ Tuition: £3,500 / US$4,500/year
✦ Location: 3 Lady Lawson St, Edinburgh EH3 9DS, United Kingdom
✦ Campus examinations are charged
✦ In-person seminars, library & student services

Apply for campus →

What You Will Learn

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
Hold a responsible-AI line on data, bias, evaluation and cost

Curriculum

Module 1: Foundations of Intelligent Systems

Code	Course	Credits · hours
BX71101	Mathematics & Probability for AI Mathematics & Probability for AI (BX71101) is a 3-credit course on Graduate Certificate in AI for Managers, 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. BX71101-M1 · The question, the unit of analysis, and data quality — Mathematics & Probability for AI BX71101-M2 · Features, baselines, and error — Mathematics & Probability for AI BX71101-M3 · Validation, leakage, and what the model must not decide — Mathematics & Probability for AI BX71101-M4 · Monitoring, drift, and human review — Mathematics & Probability for AI BX71101-M5 · Interpretation for a non-technical decision maker — Mathematics & Probability for AI BX71101-M6 · A supervised project with method, result, and limitation — Mathematics & Probability for AI	3 · 30 h
BX71102	Machine Learning Theory & Practice Machine Learning Theory & Practice (BX71102) is a 3-credit course on Graduate Certificate in AI for Managers, 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. BX71102-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice BX71102-M2 · Features, baselines, and error — Machine Learning Theory & Practice BX71102-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice BX71102-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice BX71102-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice BX71102-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice	3 · 30 h

Code

Course

Credits · hours

BX71101

Mathematics & Probability for AI

Mathematics & Probability for AI (BX71101) is a 3-credit course on Graduate Certificate in AI for Managers, 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.

BX71101-M1 · The question, the unit of analysis, and data quality — Mathematics & Probability for AI
BX71101-M2 · Features, baselines, and error — Mathematics & Probability for AI
BX71101-M3 · Validation, leakage, and what the model must not decide — Mathematics & Probability for AI
BX71101-M4 · Monitoring, drift, and human review — Mathematics & Probability for AI
BX71101-M5 · Interpretation for a non-technical decision maker — Mathematics & Probability for AI
BX71101-M6 · A supervised project with method, result, and limitation — Mathematics & Probability for AI

3 · 30 h

BX71102

Machine Learning Theory & Practice

Machine Learning Theory & Practice (BX71102) is a 3-credit course on Graduate Certificate in AI for Managers, 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.

BX71102-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice
BX71102-M2 · Features, baselines, and error — Machine Learning Theory & Practice
BX71102-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice
BX71102-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice
BX71102-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice
BX71102-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice

3 · 30 h

Module 2: Advanced Methods

Code	Course	Credits · hours
BX71105	Deep Learning & Representation Learning Deep Learning & Representation Learning (BX71105) is a 3-credit course on Graduate Certificate in AI for Managers, 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. BX71105-M1 · The question, the unit of analysis, and data quality — Deep Learning & Representation Learning BX71105-M2 · Features, baselines, and error — Deep Learning & Representation Learning BX71105-M3 · Validation, leakage, and what the model must not decide — Deep Learning & Representation Learning BX71105-M4 · Monitoring, drift, and human review — Deep Learning & Representation Learning BX71105-M5 · Interpretation for a non-technical decision maker — Deep Learning & Representation Learning BX71105-M6 · A supervised project with method, result, and limitation — Deep Learning & Representation Learning	3 · 30 h

Code

Course

Credits · hours

BX71105

Deep Learning & Representation Learning

Deep Learning & Representation Learning (BX71105) is a 3-credit course on Graduate Certificate in AI for Managers, 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.

BX71105-M1 · The question, the unit of analysis, and data quality — Deep Learning & Representation Learning
BX71105-M2 · Features, baselines, and error — Deep Learning & Representation Learning
BX71105-M3 · Validation, leakage, and what the model must not decide — Deep Learning & Representation Learning
BX71105-M4 · Monitoring, drift, and human review — Deep Learning & Representation Learning
BX71105-M5 · Interpretation for a non-technical decision maker — Deep Learning & Representation Learning
BX71105-M6 · A supervised project with method, result, and limitation — Deep Learning & Representation Learning

3 · 30 h

Module 3: Systems & Governance

Code	Course	Credits · hours
BX71109	MLOps, Evaluation & Production AI MLOps, Evaluation & Production AI (BX71109) is a 3-credit course on Graduate Certificate in AI for Managers, 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. BX71109-M1 · The question, the unit of analysis, and data quality — MLOps, Evaluation & Production AI BX71109-M2 · Features, baselines, and error — MLOps, Evaluation & Production AI BX71109-M3 · Validation, leakage, and what the model must not decide — MLOps, Evaluation & Production AI BX71109-M4 · Monitoring, drift, and human review — MLOps, Evaluation & Production AI BX71109-M5 · Interpretation for a non-technical decision maker — MLOps, Evaluation & Production AI BX71109-M6 · A supervised project with method, result, and limitation — MLOps, Evaluation & Production AI	3 · 30 h

Code

Course

Credits · hours

BX71109

MLOps, Evaluation & Production AI

MLOps, Evaluation & Production AI (BX71109) is a 3-credit course on Graduate Certificate in AI for Managers, 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.

BX71109-M1 · The question, the unit of analysis, and data quality — MLOps, Evaluation & Production AI
BX71109-M2 · Features, baselines, and error — MLOps, Evaluation & Production AI
BX71109-M3 · Validation, leakage, and what the model must not decide — MLOps, Evaluation & Production AI
BX71109-M4 · Monitoring, drift, and human review — MLOps, Evaluation & Production AI
BX71109-M5 · Interpretation for a non-technical decision maker — MLOps, Evaluation & Production AI
BX71109-M6 · A supervised project with method, result, and limitation — MLOps, Evaluation & Production AI

3 · 30 h

Module 4: Integration & Capstone

Code	Course	Credits · hours
BX71113	Research Methods for Applied AI Research Methods for Applied AI (BX71113) is a 3-credit course on Graduate Certificate in AI for Managers, 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. BX71113-M1 · The question, the unit of analysis, and data quality — Research Methods for Applied AI BX71113-M2 · Features, baselines, and error — Research Methods for Applied AI BX71113-M3 · Validation, leakage, and what the model must not decide — Research Methods for Applied AI BX71113-M4 · Monitoring, drift, and human review — Research Methods for Applied AI BX71113-M5 · Interpretation for a non-technical decision maker — Research Methods for Applied AI BX71113-M6 · A supervised project with method, result, and limitation — Research Methods for Applied AI	3 · 30 h

Code

Course

Credits · hours

BX71113

Research Methods for Applied AI

Research Methods for Applied AI (BX71113) is a 3-credit course on Graduate Certificate in AI for Managers, 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.

BX71113-M1 · The question, the unit of analysis, and data quality — Research Methods for Applied AI
BX71113-M2 · Features, baselines, and error — Research Methods for Applied AI
BX71113-M3 · Validation, leakage, and what the model must not decide — Research Methods for Applied AI
BX71113-M4 · Monitoring, drift, and human review — Research Methods for Applied AI
BX71113-M5 · Interpretation for a non-technical decision maker — Research Methods for Applied AI
BX71113-M6 · A supervised project with method, result, and limitation — Research Methods for Applied AI

3 · 30 h

Career Paths

Machine-learning engineer
AI product manager
Applied research scientist
Data science lead
AI governance and assurance roles

Programme Highlights

Hands-on modelling, deployment and evaluation
Responsible-AI, safety and governance throughout
A capstone that ships a working, monitored model
No black-box theatre — you explain what the system does and does not do

Assessment

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.

Admission requirements

Academic entry

Bachelor's degree, or three years of relevant professional experience
Identity document for remote identity-checked assessment enrolment

English language

English proficiency: IELTS 5.5 or equivalent

Documents

Brief statement of interest (250 words)
CV or résumé

Programme questions

How long is the Graduate Certificate in AI for Managers?

The published duration is 9 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.

Can I study this from my country?

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.

What does it cost?

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.

What are the entry requirements?

Requirements are grouped on this page (academic, English, documents). Equivalent qualifications are considered. English may be waived after prior English-medium study.

How is the programme assessed?

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.

Will this award be recognised where I live?

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.

How do I apply?

Start an application on this website. Progress is saved from the first step. Admissions: admissions@brigant.uk.

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