Master of Science in AI and Machine Learning
Investigate an artificial-intelligence problem through advanced study and a supervised research project.
About the course
Investigate an artificial-intelligence problem through advanced study and a supervised research project. The proposed masters outline emphasises a defensible research question, credible baselines and careful evaluation. Southern African applications should consider language, data availability, representativeness and practical use, with conclusions restricted to what the experiments support.
What you'll learn
Critically evaluate research in a selected AI field.
Design a reproducible investigation with suitable baselines and evaluation methods.
Analyse errors, uncertainty and ethical implications.
Defend an independent research contribution and its practical limitations.
Requirements
Entry requires a relevant degree with suitable advanced study or a postgraduate diploma, subject to BMIT assessment of research preparation and supervision. English Language is required.
African Kalanga is a compulsory graduation subject in every year of this programme. BMIT confirms the approved entry route, any recognition of prior learning and access to required practical facilities before enrolment.
Course Content
The subjects are grouped by learning stage, from foundations to specialist study and supervised application. Complete earlier foundations before the related advanced work. Clinical, laboratory, field and workplace subjects use approved facilities and supervision appropriate to the programme.
3 learning levels 18 subjects
Select a subject to read its learning outcomes, main topics, practical task and assessment.
Level 1: Postgraduate disciplinary study
Africa Indigenous Language: African Kalanga 101Compulsory graduation subject · Year 1
Develop African Kalanga through foundations for engineering, science and technology. Practise with authentic tasks, feedback and reflection across the programme year.
Learning outcomes
- Use African Kalanga accurately for Year 1 study and practice.
- Explain language choices to the intended audience.
- Revise your work using speaker and facilitator feedback.
Main topics
- Pronunciation
- Everyday vocabulary
- Listening
- Reading and respectful introductions
Practical task
Record a short introduction and explain its meaning to a peer.
Assessment
Submit the practical work, a brief reflection and evidence of language review. Assessment checks meaning, audience fit and improvement after feedback.
Entrepreneurship, Creativity and Innovation 101Compulsory core module · Level 1
Identify a real need, study the people affected and develop a responsible idea for a product, service or process. Apply critical analysis and relevant research to a specialist setting. Apply this learning to Master of Science in AI and Machine Learning through an authentic brief and feedback from an appropriate audience.
Learning outcomes
- Investigate a defined opportunity and explain the evidence behind it.
- Develop and refine a creative response that fits the people and setting.
- Evaluate feasibility, ethical implications and the quality of the result.
Main topics
- Opportunity recognition
- Creative problem framing
- Community and user research
- Ethics and inclusion
- Idea generation and selection
Practical task
Investigate a need related to Master of Science in AI and Machine Learning. Interview or observe intended users with consent, compare possible responses and present one justified concept.
Assessment
Submit an opportunity brief, research notes, a concept sketch and a reflection on feedback. Support your decisions with current research and a critical account of limitations. Assessment considers the quality of the evidence, the response to feedback, feasibility and your individual explanation.
African History and Pan Africanism 101Compulsory core module · Level 1
Study African history through oral, written and archaeological evidence. Examine the diversity of African societies, knowledge and exchange, then trace the emergence of Pan African ideas about identity, solidarity and self-determination.
Learning outcomes
- Place selected African societies and events within a supported historical timeline.
- Compare historical sources and identify perspective, evidence and uncertainty.
- Explain the foundations of Pan Africanism and the contributions of Africans and the diaspora.
Main topics
- Historical inquiry, chronology, oral traditions and source evaluation
- African civilisations, states, trade, livelihoods and knowledge systems
- Southern African history, including Mapungubgwe and Great Zimbabwe
- Slavery, the African diaspora and changing ideas about identity
- Origins of Pan Africanism, solidarity, self-determination and cultural expression
Practical task
Create an annotated timeline or digital learning poster on a selected African society or historical development. Compare at least two credible sources and explain one connection with your field of study.
Assessment
Submit the timeline or poster, an annotated source list and a short explanation of the evidence. Assessment considers chronology, historical accuracy, source judgement and the clarity of your interpretation.
Learning resources
Probability and Statistical Modelling
Examine random variables, distributions and their relationship within probability and statistical modelling. The subject develops conditional probability and likelihood, then examines bayesian reasoning and model checking. Compare statistical models using a transparent teaching dataset.
Learning outcomes
- Explain random variables and distributions using an appropriate example.
- Analyse a subject-related problem involving conditional probability and likelihood.
- Present reasoned evidence addressing bayesian reasoning and model checking.
Main topics
- Random variables
- Distributions
- Conditional probability
- Likelihood
- Bayesian reasoning
- Model checking
Practical task
Compare statistical models using a transparent teaching dataset.
Assessment
Submit calculations, model checks and an interpretation.
Optimisation Methods
Develop your understanding of optimisation methods through objective functions and constraints. The subject develops gradient methods and linear programming, then examines convexity concepts and sensitivity. Solve a constrained allocation or model-fitting problem.
Learning outcomes
- Explain objective functions and constraints using an appropriate example.
- Analyse a subject-related problem involving gradient methods and linear programming.
- Present reasoned evidence addressing convexity concepts and sensitivity.
Main topics
- Objective functions
- Constraints
- Gradient methods
- Linear programming
- Convexity concepts
- Sensitivity
Practical task
Solve a constrained allocation or model-fitting problem.
Assessment
Submit a reproducible solution and sensitivity discussion.
Machine Learning
Study learning tasks and features as foundations for machine learning. The subject develops training and generalisation, then examines model selection and error analysis. Compare a baseline and candidate model on appropriately separated data.
Learning outcomes
- Explain learning tasks and features using an appropriate example.
- Analyse a subject-related problem involving training and generalisation.
- Present reasoned evidence addressing model selection and error analysis.
Main topics
- Learning tasks
- Features
- Training
- Generalisation
- Model selection
- Error analysis
Practical task
Compare a baseline and candidate model on appropriately separated data.
Assessment
Submit reproducible experiments and an error analysis.
Deep Learning
Examine neural representations, optimisation and their relationship within deep learning. The subject develops regularisation and model architectures, then examines training diagnostics and evaluation. Investigate a small model using an approved public dataset.
Learning outcomes
- Explain neural representations and optimisation using an appropriate example.
- Analyse a subject-related problem involving regularisation and model architectures.
- Present reasoned evidence addressing training diagnostics and evaluation.
Main topics
- Neural representations
- Optimisation
- Regularisation
- Model architectures
- Training diagnostics
- Evaluation
Practical task
Investigate a small model using an approved public dataset.
Assessment
Submit code, learning curves and a reasoned model comparison.
Level 2: Specialist analysis and application
Entrepreneurship, Creativity and Innovation 201Compulsory core module · Level 2
Turn a promising idea into a testable proposal using evidence, creative methods and basic resource planning. Apply critical analysis and relevant research to a specialist setting. Apply this learning to Master of Science in AI and Machine Learning through an authentic brief and feedback from an appropriate audience.
Learning outcomes
- Investigate a defined opportunity and explain the evidence behind it.
- Develop and refine a creative response that fits the people and setting.
- Evaluate feasibility, ethical implications and the quality of the result.
Main topics
- Co-design and prototyping
- Value proposition
- Feasibility and costs
- Risk and responsible practice
- Testing and iteration
Practical task
Develop a small prototype or service model for a Master of Science in AI and Machine Learning problem. Test it with relevant users, record feedback and revise the proposal.
Assessment
Submit a prototype, a feasibility note, user feedback and a record of revisions. Support your decisions with current research and a critical account of limitations. Assessment considers the quality of the evidence, the response to feedback, feasibility and your individual explanation.
African History and Pan Africanism 201Compulsory core module · Level 2
Examine colonial change, resistance and liberation across Africa, with attention to Zimbabwe and South Africa. Compare Pan African movements, organisations and debates, including contributions from women, workers and diaspora communities.
Learning outcomes
- Analyse the effects of colonial rule on land, labour, institutions and social relations.
- Compare liberation movements and Pan African arguments using contextual evidence.
- Explain how different groups shaped independence, solidarity and debates about freedom.
Main topics
- Colonial rule, land, labour, education and forms of resistance
- Nationalism, liberation and the end of colonial rule and apartheid
- Pan African congresses, organisations and contrasting interpretations
- Women, labour movements and diaspora participation
- The Organisation of African Unity and debates about unity, sovereignty and development
Practical task
Develop a comparative case study of a liberation issue or Pan African debate relevant to Southern Africa. Use a historical document alongside a scholarly account and explain differences in purpose and interpretation.
Assessment
Submit the case study, a source comparison and a reflection on your conclusions. Assessment considers context, evidence, recognition of different perspectives and the quality of your argument.
Learning resources
Knowledge Representation and Reasoning
Develop your understanding of knowledge representation and reasoning through logic and rules. The subject develops ontologies and inference, then examines uncertainty and knowledge quality. Build a small knowledge model and test it against defined questions.
Learning outcomes
- Explain logic and rules using an appropriate example.
- Analyse a subject-related problem involving ontologies and inference.
- Present reasoned evidence addressing uncertainty and knowledge quality.
Main topics
- Logic
- Rules
- Ontologies
- Inference
- Uncertainty
- Knowledge quality
Practical task
Build a small knowledge model and test it against defined questions.
Assessment
Submit the model, test cases and limitations.
Natural Language Processing
Study text representation and linguistic variation as foundations for natural language processing. The subject develops language modelling and classification, then examines translation evaluation and language-data ethics. Analyse an authorised language dataset, including relevant regional variation.
Learning outcomes
- Explain text representation and linguistic variation using an appropriate example.
- Analyse a subject-related problem involving language modelling and classification.
- Present reasoned evidence addressing translation evaluation and language-data ethics.
Main topics
- Text representation
- Linguistic variation
- Language modelling
- Classification
- Translation evaluation
- Language-data ethics
Practical task
Analyse an authorised language dataset, including relevant regional variation.
Assessment
Submit an evaluated language-processing project with documented errors.
Computer Vision
Examine image representation, features and their relationship within computer vision. The subject develops recognition and detection, then examines image evaluation and dataset bias. Compare image-analysis methods on an authorised teaching dataset.
Learning outcomes
- Explain image representation and features using an appropriate example.
- Analyse a subject-related problem involving recognition and detection.
- Present reasoned evidence addressing image evaluation and dataset bias.
Main topics
- Image representation
- Features
- Recognition
- Detection
- Image evaluation
- Dataset bias
Practical task
Compare image-analysis methods on an authorised teaching dataset.
Assessment
Submit experiments and an error-focused visual report.
Responsible AI and Data Governance
Develop your understanding of responsible ai and data governance through consent and privacy. The subject develops bias and explainability, then examines human oversight and deployment accountability. Audit a proposed AI use case for data and decision risks.
Learning outcomes
- Explain consent and privacy using an appropriate example.
- Analyse a subject-related problem involving bias and explainability.
- Present reasoned evidence addressing human oversight and deployment accountability.
Main topics
- Consent
- Privacy
- Bias
- Explainability
- Human oversight
- Deployment accountability
Practical task
Audit a proposed AI use case for data and decision risks.
Assessment
Submit a documented governance and evaluation plan.
Level 3: Supervised research or applied integration
Africa Indigenous Language: African Kalanga 201Compulsory graduation subject · Year 2
Develop African Kalanga through applied communication for engineering, science and technology. Practise with authentic tasks, feedback and reflection across the programme year.
Learning outcomes
- Use African Kalanga accurately for Year 2 study and practice.
- Explain language choices to the intended audience.
- Revise your work using speaker and facilitator feedback.
Main topics
- Workplace vocabulary
- Questions
- Instructions
- Short messages and feedback
Practical task
Prepare and test a bilingual message for a realistic community or workplace situation.
Assessment
Submit the practical work, a brief reflection and evidence of language review. Assessment checks meaning, audience fit and improvement after feedback.
Entrepreneurship, Creativity and Innovation 301Compulsory core module · Level 3
Plan and evaluate the practical introduction of an innovation in a relevant organisation or community. Apply critical analysis and relevant research to a specialist setting. Apply this learning to Master of Science in AI and Machine Learning through an authentic brief and feedback from an appropriate audience.
Learning outcomes
- Investigate a defined opportunity and explain the evidence behind it.
- Develop and refine a creative response that fits the people and setting.
- Evaluate feasibility, ethical implications and the quality of the result.
Main topics
- Implementation planning
- Stakeholder engagement
- Resources and finance
- Monitoring and impact
- Learning from a pilot
Practical task
Prepare and defend a pilot plan for an innovation relevant to Master of Science in AI and Machine Learning. Set measures of success, identify risks and explain how results will guide improvement.
Assessment
Submit a pilot plan, budget assumptions, evaluation measures and an individual rationale. Support your decisions with current research and a critical account of limitations. Assessment considers the quality of the evidence, the response to feedback, feasibility and your individual explanation.
Advanced Research Design
Study research positioning and theoretical frameworks as foundations for advanced research design. The subject develops method comparison and validity, then examines research ethics and feasibility. Defend an advanced study design against plausible alternatives.
Learning outcomes
- Explain research positioning and theoretical frameworks using an appropriate example.
- Analyse a subject-related problem involving method comparison and validity.
- Present reasoned evidence addressing research ethics and feasibility.
Main topics
- Research positioning
- Theoretical frameworks
- Method comparison
- Validity
- Research ethics
- Feasibility
Practical task
Defend an advanced study design against plausible alternatives.
Assessment
Submit a research protocol and critical methodological defence.
Research Proposal and Protocol
Examine problem significance, literature synthesis and their relationship within research proposal and protocol. The subject develops research questions and protocol development, then examines ethics approval and work plan. Develop a supervised research protocol and defend its feasibility.
Learning outcomes
- Explain problem significance and literature synthesis using an appropriate example.
- Analyse a subject-related problem involving research questions and protocol development.
- Present reasoned evidence addressing ethics approval and work plan.
Main topics
- Problem significance
- Literature synthesis
- Research questions
- Protocol development
- Ethics approval
- Work plan
Practical task
Develop a supervised research protocol and defend its feasibility.
Assessment
Submit the approved proposal and a research seminar presentation.
Dissertation Research and Defence
Develop your understanding of dissertation research and defence through data stewardship and analysis. The subject develops interpretation and research contribution, then examines limitations and scholarly communication. Complete approved research under supervision and retain reproducible evidence.
Learning outcomes
- Explain data stewardship and analysis using an appropriate example.
- Analyse a subject-related problem involving interpretation and research contribution.
- Present reasoned evidence addressing limitations and scholarly communication.
Main topics
- Data stewardship
- Analysis
- Interpretation
- Research contribution
- Limitations
- Scholarly communication
Practical task
Complete approved research under supervision and retain reproducible evidence.
Assessment
Submit a dissertation and defend methods, findings and limitations orally.
Practical Learning
Authentic e-learning at BMIT
BMIT uses Authentic E-Learning to connect your studies with practical problems in your field. You use digital resources to investigate a realistic brief, make decisions and produce work for a defined purpose and audience. Your qualification's activities below put this approach into practice.
How you will learn
The approach draws on nine elements of authentic learning:
- Realistic contexts: Work with situations relevant to your profession or community.
- Purposeful tasks: Investigate a substantial problem with more than one possible solution.
- Expert examples: Examine demonstrations and examples of professional reasoning.
- Different perspectives: Compare sources and consider the needs of different people.
- Collaboration: Share ideas and review work with peers, while showing your own contribution.
- Reflection: Record what worked, what changed and what needs improvement.
- Explanation: Present your reasoning and support your decisions with evidence.
- Guidance: Use tutor feedback and structured support as you develop independence.
- Authentic assessment: Demonstrate learning through the task and its practical outcome.
From a brief to a practical outcome
Start by identifying the problem, intended audience and assessment criteria. Investigate relevant evidence, plan your response and develop an initial solution. Use discussion and feedback to test your ideas, then revise your work and explain your choices.
Digital tools support research, collaboration, simulation and the presentation of evidence. Your output depends on the module. Examples include a business proposal, technical design, tested program, investigation report or community resource. Keep drafts, calculations, test results and reflections to show how your work developed.
The module brief sets the required tasks, individual and group responsibilities, submission format and assessment criteria. The practical activities and supervision requirements for this qualification are set out below.
Research informing this approach
- Herrington, J. Authentic Learning: nine elements of authentic learning.
- Lombardi, M. M. (2007). Authentic learning for the 21st century: An overview. EDUCAUSE Learning Initiative.
- Parker, J., Maor, D. and Herrington, J. (2013). Authentic online learning: Aligning learner needs, pedagogy and technology. Issues in Educational Research, 23(2), 227–241.
Use guided online cases and data tasks to prepare for practical work. Laboratory, field or equipment tasks require suitable facilities and supervision.
What you will practise
- Machine Learning: Compare a baseline and candidate model on appropriately separated data.
- Computer Vision: Compare image-analysis methods on an authorised teaching dataset.
- Research Proposal and Protocol: Develop a supervised research protocol and defend its feasibility.
Applied project
Research requires appropriate computing access, an academic supervisor and permission to use the selected data. Maintain versioned code, data documentation and experimental records. Personal or sensitive data needs formal review. Claims about progress should distinguish measured performance from assumptions about deployment or generalisation.
Evidence and feedback
- A completed practical task with an explanation of the method, evidence and decisions.
- An applied project or research proposal addressing a defined Southern African institutional or community problem.
- An independently assessed report or dissertation with an oral presentation, according to an approved BMIT route and assessment policy.
Assessment considers the practical work you submit, the evidence behind your decisions and how you respond to feedback.
African History and Pan Africanism practical learning
- African History and Pan Africanism 101: Create an annotated timeline or digital learning poster on a selected African society or historical development. Compare at least two credible sources and explain one connection with your field of study.
- African History and Pan Africanism 201: Develop a comparative case study of a liberation issue or Pan African debate relevant to Southern Africa. Use a historical document alongside a scholarly account and explain differences in purpose and interpretation.
Career Prospects
These examples show work across this qualification pathway, from support roles to specialist and management posts. Your entry point depends on your study, experience and employer requirements. Regulated roles require separate recognition or registration.
Top Career Paths and Job Titles
- Data assistant: Cleans datasets and documents how information was collected and prepared.
- Machine learning developer: Builds and tests models against defined business or research questions.
- AI and data science specialist: Evaluates model performance, risks and deployment decisions with stakeholders.
- Data engineer: Builds reliable data pipelines and checks the quality of information used by models.
- AI product lead: Directs teams that test, deploy and monitor AI systems against real needs and risks.
Growth and Leadership Trajectory
- Starting practice: After meeting entry and any registration requirements, begin with the work represented by Data assistant. Build supervised experience and keep a record of reliable work.
- Specialist progression: Relevant study and evidence from more complex projects support a move towards Machine learning developer or AI and data science specialist.
- Leadership: With substantial experience, a role such as AI product lead adds responsibility for people, quality, budgets or service decisions.
Further Education to Boost Prospects
- Doctoral study: A PhD or other suitable doctorate in artificial intelligence and data science is a possible later route. BMIT does not currently list a doctoral programme.
Professional Certifications and Registrations
- Role-specific requirements: No single statutory credential covers every role in artificial intelligence and data science. Employers may require a relevant short course, supervised experience or role-specific licence. Check the requirements for the job and country before applying.
Potential Government and Industrial Employers
- Entrepreneurship and Small Business Management: Start or manage a small venture related to this field. Regulated work still requires the relevant professional registration or licence.
- Private and commercial organisations: Software and analytics companies.
- Specialist employers: Banks, telecoms and digital service providers.
- Research and community organisations: Research laboratories and technology consultancies.
- Government departments and public bodies: Government data, statistics and digital-service units.