Certificate

Graduate Certificate in AI for Managers

A focused, stackable credential that proves one capability fast.

Online or on campus

9 months120 creditsEnglish (dual pathway: London 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 Haverton University teaches this award to one academic standard whether you study on campus in London 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 Haverton University 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

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Study online

100% tuition-free online

Online tuition is free. Online examinations and certificates are 100% free and sponsored.

✦ Tuition: £0 / $0
✦ Examinations & certificates are 100% free & sponsored
✦ Learn from anywhere · Live online seminars

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On-campus study

Study at our campus

Prefer to learn at our London campus? Campus tuition is published on each programme page. All examinations are 100% free of charge.

✦ Tuition: £3,500 / US$4,500/year
✦ Location: Du Cane Rd, London W12 0NN, United Kingdom
✦ Campus examinations are charged
✦ In-person seminars, library & student services

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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 course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree. 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	15 · 150 h
BX71102	Machine Learning Theory & Practice Machine Learning Theory & Practice (BX71102) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree. 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	15 · 150 h
BX581	A question and a dataset A question and a dataset (BX581) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree. BX581-M1 · The job this module prepares the student to do BX581-M2 · The terms used in that job BX581-M3 · A workplace example BX581-M4 · A mistake a new practitioner makes BX581-M5 · The document or tool used BX581-M6 · What the module does not qualify the student to do BX581-M7 · A check against a standard BX581-M8 · The submission: a workplace glossary and example. The assessed course is A question and a dataset.	15 · 150 h
BX582	Cleaning and a rejected row Cleaning and a rejected row (BX582) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree. BX582-M1 · The method, in the order it is used at work BX582-M2 · A worked example BX582-M3 · The step that must not be skipped BX582-M4 · Evidence the step was done BX582-M5 · A second example with a different result BX582-M6 · A shortcut and why it fails BX582-M7 · Feedback and a correction BX582-M8 · The submission: a worked method sheet. The assessed course is Cleaning and a rejected row.	15 · 150 h
BX583	A baseline result A baseline result (BX583) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree. BX583-M1 · The record the workplace keeps BX583-M2 · Who writes it and who reads it BX583-M3 · An error in the record and how it is found BX583-M4 · What the record cannot prove BX583-M5 · Retention, access, or confidentiality as it applies here BX583-M6 · A better entry and a worse one BX583-M7 · The check a supervisor would make BX583-M8 · The submission: a completed record with a note. The assessed course is A baseline result.	15 · 150 h

Module 2: Advanced Methods

Code	Course	Credits · hours
BX71105	Deep Learning & Representation Learning Deep Learning & Representation Learning (BX71105) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree. 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	15 · 150 h

Code

Course

Credits · hours

BX71105

Deep Learning & Representation Learning

Deep Learning & Representation Learning (BX71105) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.

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

15 · 150 h

Module 3: Systems & Governance

Code	Course	Credits · hours
BX71109	MLOps, Evaluation & Production AI MLOps, Evaluation & Production AI (BX71109) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree. 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	15 · 150 h

Code

Course

Credits · hours

BX71109

MLOps, Evaluation & Production AI

MLOps, Evaluation & Production AI (BX71109) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.

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

15 · 150 h

Module 4: Integration & Capstone

Code	Course	Credits · hours
BX71113	Research Methods for Applied AI Research Methods for Applied AI (BX71113) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree. 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	15 · 150 h

Code

Course

Credits · hours

BX71113

Research Methods for Applied AI

Research Methods for Applied AI (BX71113) is a course on Graduate Certificate in AI for Managers. It carries 15 credits and 150 notional learning hours, at 10 hours per credit. The course is a professional certificate module. The submission is a workplace artefact a marker can check against the chapters. The chapters are the syllabus. Each chapter is a class of work this course assesses. Preparation is the reading, dataset, or case for that chapter. Seminar time is spent on the decision, the calculation, or the draft. The student revises after feedback. A pass requires the method named in the chapters, evidence a marker can check, and a stated limit on what the result does not show. Haverton University teaches this course on the published route for this degree.

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

15 · 150 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: London 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 Haverton. 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. All online degree examinations and certificates are 100% free and sponsored — the only cost for online study is the application fee. Campus tuition and campus examinations are charged separately. Check the Fees page for details.

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. Haverton University 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@haverton.org.

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