MSc

MSc Applied AI for Public Services

MSc Applied AI for Public Services — an accredited Master's for people doing this work, not just reading about it.

Online or on campus

12 months180 creditsEnglish (dual pathway: London campus or tuition-free online; one academic standard)

Overview

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 London. 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 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 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

Recommended

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

Apply online →

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: £9,500 / US$12,000/year
✦ Location: Du Cane Rd, London W12 0NN, 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
BX70101	Mathematics & Probability for AI Mathematics & Probability for AI (BX70101) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70101-M1 · The question, the unit of analysis, and data quality — Mathematics & Probability for AI BX70101-M2 · Features, baselines, and error — Mathematics & Probability for AI BX70101-M3 · Validation, leakage, and what the model must not decide — Mathematics & Probability for AI BX70101-M4 · Monitoring, drift, and human review — Mathematics & Probability for AI BX70101-M5 · Interpretation for a non-technical decision maker — Mathematics & Probability for AI BX70101-M6 · A supervised project with method, result, and limitation — Mathematics & Probability for AI	18 · 180 h
BX70102	Machine Learning Theory & Practice Machine Learning Theory & Practice (BX70102) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70102-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice BX70102-M2 · Features, baselines, and error — Machine Learning Theory & Practice BX70102-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice BX70102-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice BX70102-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice BX70102-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice	18 · 180 h
BX70103	Programming Intelligent Systems Programming Intelligent Systems (BX70103) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70103-M1 · The question, the unit of analysis, and data quality — Programming Intelligent Systems BX70103-M2 · Features, baselines, and error — Programming Intelligent Systems BX70103-M3 · Validation, leakage, and what the model must not decide — Programming Intelligent Systems BX70103-M4 · Monitoring, drift, and human review — Programming Intelligent Systems BX70103-M5 · Interpretation for a non-technical decision maker — Programming Intelligent Systems BX70103-M6 · A supervised project with method, result, and limitation — Programming Intelligent Systems	18 · 180 h

Code

Course

Credits · hours

BX70101

Mathematics & Probability for AI

Mathematics & Probability for AI (BX70101) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70101-M1 · The question, the unit of analysis, and data quality — Mathematics & Probability for AI
BX70101-M2 · Features, baselines, and error — Mathematics & Probability for AI
BX70101-M3 · Validation, leakage, and what the model must not decide — Mathematics & Probability for AI
BX70101-M4 · Monitoring, drift, and human review — Mathematics & Probability for AI
BX70101-M5 · Interpretation for a non-technical decision maker — Mathematics & Probability for AI
BX70101-M6 · A supervised project with method, result, and limitation — Mathematics & Probability for AI

18 · 180 h

BX70102

Machine Learning Theory & Practice

Machine Learning Theory & Practice (BX70102) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70102-M1 · The question, the unit of analysis, and data quality — Machine Learning Theory & Practice
BX70102-M2 · Features, baselines, and error — Machine Learning Theory & Practice
BX70102-M3 · Validation, leakage, and what the model must not decide — Machine Learning Theory & Practice
BX70102-M4 · Monitoring, drift, and human review — Machine Learning Theory & Practice
BX70102-M5 · Interpretation for a non-technical decision maker — Machine Learning Theory & Practice
BX70102-M6 · A supervised project with method, result, and limitation — Machine Learning Theory & Practice

18 · 180 h

BX70103

Programming Intelligent Systems

Programming Intelligent Systems (BX70103) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70103-M1 · The question, the unit of analysis, and data quality — Programming Intelligent Systems
BX70103-M2 · Features, baselines, and error — Programming Intelligent Systems
BX70103-M3 · Validation, leakage, and what the model must not decide — Programming Intelligent Systems
BX70103-M4 · Monitoring, drift, and human review — Programming Intelligent Systems
BX70103-M5 · Interpretation for a non-technical decision maker — Programming Intelligent Systems
BX70103-M6 · A supervised project with method, result, and limitation — Programming Intelligent Systems

18 · 180 h

Module 2: Advanced Methods

Code	Course	Credits · hours
BX70105	Deep Learning & Representation Learning Deep Learning & Representation Learning (BX70105) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70105-M1 · The question, the unit of analysis, and data quality — Deep Learning & Representation Learning BX70105-M2 · Features, baselines, and error — Deep Learning & Representation Learning BX70105-M3 · Validation, leakage, and what the model must not decide — Deep Learning & Representation Learning BX70105-M4 · Monitoring, drift, and human review — Deep Learning & Representation Learning BX70105-M5 · Interpretation for a non-technical decision maker — Deep Learning & Representation Learning BX70105-M6 · A supervised project with method, result, and limitation — Deep Learning & Representation Learning	18 · 180 h
BX70106	Natural Language Processing & Large Language Models Natural Language Processing & Large Language Models (BX70106) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70106-M1 · The question, the unit of analysis, and data quality — Natural Language Processing & Large Language Models BX70106-M2 · Features, baselines, and error — Natural Language Processing & Large Language Models BX70106-M3 · Validation, leakage, and what the model must not decide — Natural Language Processing & Large Language Models BX70106-M4 · Monitoring, drift, and human review — Natural Language Processing & Large Language Models BX70106-M5 · Interpretation for a non-technical decision maker — Natural Language Processing & Large Language Models BX70106-M6 · A supervised project with method, result, and limitation — Natural Language Processing & Large Language Models	18 · 180 h
BX70107	Computer Vision & Multimodal AI Computer Vision & Multimodal AI (BX70107) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70107-M1 · The question, the unit of analysis, and data quality — Computer Vision & Multimodal AI BX70107-M2 · Features, baselines, and error — Computer Vision & Multimodal AI BX70107-M3 · Validation, leakage, and what the model must not decide — Computer Vision & Multimodal AI BX70107-M4 · Monitoring, drift, and human review — Computer Vision & Multimodal AI BX70107-M5 · Interpretation for a non-technical decision maker — Computer Vision & Multimodal AI BX70107-M6 · A supervised project with method, result, and limitation — Computer Vision & Multimodal AI	18 · 180 h

Code

Course

Credits · hours

BX70105

Deep Learning & Representation Learning

Deep Learning & Representation Learning (BX70105) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70105-M1 · The question, the unit of analysis, and data quality — Deep Learning & Representation Learning
BX70105-M2 · Features, baselines, and error — Deep Learning & Representation Learning
BX70105-M3 · Validation, leakage, and what the model must not decide — Deep Learning & Representation Learning
BX70105-M4 · Monitoring, drift, and human review — Deep Learning & Representation Learning
BX70105-M5 · Interpretation for a non-technical decision maker — Deep Learning & Representation Learning
BX70105-M6 · A supervised project with method, result, and limitation — Deep Learning & Representation Learning

18 · 180 h

BX70106

Natural Language Processing & Large Language Models

Natural Language Processing & Large Language Models (BX70106) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70106-M1 · The question, the unit of analysis, and data quality — Natural Language Processing & Large Language Models
BX70106-M2 · Features, baselines, and error — Natural Language Processing & Large Language Models
BX70106-M3 · Validation, leakage, and what the model must not decide — Natural Language Processing & Large Language Models
BX70106-M4 · Monitoring, drift, and human review — Natural Language Processing & Large Language Models
BX70106-M5 · Interpretation for a non-technical decision maker — Natural Language Processing & Large Language Models
BX70106-M6 · A supervised project with method, result, and limitation — Natural Language Processing & Large Language Models

18 · 180 h

BX70107

Computer Vision & Multimodal AI

Computer Vision & Multimodal AI (BX70107) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70107-M1 · The question, the unit of analysis, and data quality — Computer Vision & Multimodal AI
BX70107-M2 · Features, baselines, and error — Computer Vision & Multimodal AI
BX70107-M3 · Validation, leakage, and what the model must not decide — Computer Vision & Multimodal AI
BX70107-M4 · Monitoring, drift, and human review — Computer Vision & Multimodal AI
BX70107-M5 · Interpretation for a non-technical decision maker — Computer Vision & Multimodal AI
BX70107-M6 · A supervised project with method, result, and limitation — Computer Vision & Multimodal AI

18 · 180 h

Module 3: Systems & Governance

Code	Course	Credits · hours
BX70109	MLOps, Evaluation & Production AI MLOps, Evaluation & Production AI (BX70109) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70109-M1 · The question, the unit of analysis, and data quality — MLOps, Evaluation & Production AI BX70109-M2 · Features, baselines, and error — MLOps, Evaluation & Production AI BX70109-M3 · Validation, leakage, and what the model must not decide — MLOps, Evaluation & Production AI BX70109-M4 · Monitoring, drift, and human review — MLOps, Evaluation & Production AI BX70109-M5 · Interpretation for a non-technical decision maker — MLOps, Evaluation & Production AI BX70109-M6 · A supervised project with method, result, and limitation — MLOps, Evaluation & Production AI	18 · 180 h
BX70110	Responsible AI, Safety & Policy Responsible AI, Safety & Policy (BX70110) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70110-M1 · The question, the unit of analysis, and data quality — Responsible AI, Safety & Policy BX70110-M2 · Features, baselines, and error — Responsible AI, Safety & Policy BX70110-M3 · Validation, leakage, and what the model must not decide — Responsible AI, Safety & Policy BX70110-M4 · Monitoring, drift, and human review — Responsible AI, Safety & Policy BX70110-M5 · Interpretation for a non-technical decision maker — Responsible AI, Safety & Policy BX70110-M6 · A supervised project with method, result, and limitation — Responsible AI, Safety & Policy	18 · 180 h

Code

Course

Credits · hours

BX70109

MLOps, Evaluation & Production AI

MLOps, Evaluation & Production AI (BX70109) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70109-M1 · The question, the unit of analysis, and data quality — MLOps, Evaluation & Production AI
BX70109-M2 · Features, baselines, and error — MLOps, Evaluation & Production AI
BX70109-M3 · Validation, leakage, and what the model must not decide — MLOps, Evaluation & Production AI
BX70109-M4 · Monitoring, drift, and human review — MLOps, Evaluation & Production AI
BX70109-M5 · Interpretation for a non-technical decision maker — MLOps, Evaluation & Production AI
BX70109-M6 · A supervised project with method, result, and limitation — MLOps, Evaluation & Production AI

18 · 180 h

BX70110

Responsible AI, Safety & Policy

Responsible AI, Safety & Policy (BX70110) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70110-M1 · The question, the unit of analysis, and data quality — Responsible AI, Safety & Policy
BX70110-M2 · Features, baselines, and error — Responsible AI, Safety & Policy
BX70110-M3 · Validation, leakage, and what the model must not decide — Responsible AI, Safety & Policy
BX70110-M4 · Monitoring, drift, and human review — Responsible AI, Safety & Policy
BX70110-M5 · Interpretation for a non-technical decision maker — Responsible AI, Safety & Policy
BX70110-M6 · A supervised project with method, result, and limitation — Responsible AI, Safety & Policy

18 · 180 h

Module 4: Integration & Capstone

Code	Course	Credits · hours
BX70113	Research Methods for Applied AI Research Methods for Applied AI (BX70113) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70113-M1 · The question, the unit of analysis, and data quality — Research Methods for Applied AI BX70113-M2 · Features, baselines, and error — Research Methods for Applied AI BX70113-M3 · Validation, leakage, and what the model must not decide — Research Methods for Applied AI BX70113-M4 · Monitoring, drift, and human review — Research Methods for Applied AI BX70113-M5 · Interpretation for a non-technical decision maker — Research Methods for Applied AI BX70113-M6 · A supervised project with method, result, and limitation — Research Methods for Applied AI	18 · 180 h
BX70114	Communicating AI to Non-Specialists Communicating AI to Non-Specialists (BX70114) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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. BX70114-M1 · The question, the unit of analysis, and data quality — Communicating AI to Non-Specialists BX70114-M2 · Features, baselines, and error — Communicating AI to Non-Specialists BX70114-M3 · Validation, leakage, and what the model must not decide — Communicating AI to Non-Specialists BX70114-M4 · Monitoring, drift, and human review — Communicating AI to Non-Specialists BX70114-M5 · Interpretation for a non-technical decision maker — Communicating AI to Non-Specialists BX70114-M6 · A supervised project with method, result, and limitation — Communicating AI to Non-Specialists	18 · 180 h

Code

Course

Credits · hours

BX70113

Research Methods for Applied AI

Research Methods for Applied AI (BX70113) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70113-M1 · The question, the unit of analysis, and data quality — Research Methods for Applied AI
BX70113-M2 · Features, baselines, and error — Research Methods for Applied AI
BX70113-M3 · Validation, leakage, and what the model must not decide — Research Methods for Applied AI
BX70113-M4 · Monitoring, drift, and human review — Research Methods for Applied AI
BX70113-M5 · Interpretation for a non-technical decision maker — Research Methods for Applied AI
BX70113-M6 · A supervised project with method, result, and limitation — Research Methods for Applied AI

18 · 180 h

BX70114

Communicating AI to Non-Specialists

Communicating AI to Non-Specialists (BX70114) is a course on MSc Applied AI for Public Services. It carries 18 credits and 180 notional learning hours, at 10 hours per credit. The course is taught at master's level. Students compare a weaker method, judge the evidence, and state what the result does not show. 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.

BX70114-M1 · The question, the unit of analysis, and data quality — Communicating AI to Non-Specialists
BX70114-M2 · Features, baselines, and error — Communicating AI to Non-Specialists
BX70114-M3 · Validation, leakage, and what the model must not decide — Communicating AI to Non-Specialists
BX70114-M4 · Monitoring, drift, and human review — Communicating AI to Non-Specialists
BX70114-M5 · Interpretation for a non-technical decision maker — Communicating AI to Non-Specialists
BX70114-M6 · A supervised project with method, result, and limitation — Communicating AI to Non-Specialists

18 · 180 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 from a recognised institution (minimum 2:2 / GPA 2.5/4.0 or an equivalent national scale)
Identity document for remote identity-checked assessment enrolment

English language

English proficiency: IELTS 5.5, TOEFL 46 or equivalent (waived for prior English-medium study). State your primary study mode without changing the academic standard.

Documents

Statement of Purpose (500 words minimum) covering goals, professional context and fit
Two academic or professional references with verifiable contact details
CV or résumé covering education and relevant work
Official transcripts (digital copies accepted at application; certified copies before award)

Programme questions

How long is the MSc Applied AI for Public Services?

The published duration is 12 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.

Recognised & Accredited

Ready to begin?MSc Applied AI for Public Services

Start an application

Loading…