Bachelor of Science in AI and Machine Learning
Build the mathematical and computing foundations needed to investigate artificial intelligence and machine learning.
About the course
Build the mathematical and computing foundations needed to investigate artificial intelligence and machine learning. The proposed degree connects algorithms with careful data preparation, evaluation and responsible use. Southern African applications should reflect available data and languages, while asking whether a simpler method would meet the same practical need.
What you'll learn
Explain the mathematical and computational basis of selected AI methods.
Prepare data and implement a reproducible analytical workflow.
Compare models using appropriate baselines and evaluation measures.
Assess ethical, social and operational limitations of an AI application.
Requirements
Recommended preparation includes mathematics, the relevant natural sciences, academic English and computer literacy. BMIT must confirm the approved admission route, any bridging study and recognition of prior learning. Source institutions retain their own admission rules. This outline does not assign a duration, credit total or qualification-framework level.
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.
4 learning stages 28 subjects
1Stage 1: Mathematical and computing foundations8 subjects
2Stage 2: Core computing and data8 subjects
3Stage 3: AI and machine learning8 subjects
4Stage 4: Integration and research4 subjects
Subjects in this programme
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.
28 subjects are listed below with a short description of each. The Subject descriptions tab contains the learning outcomes, topics, practical tasks and assessments.
Stage 1: Mathematical and computing foundations
- Differential Calculus. Examine functions, limits and their relationship within differential calculus. The subject develops differentiation and rates of change, then examines optimisation and model assumptions. Analyse a measured change and compare graphical and algebraic solutions.
- Linear Algebra. Develop your understanding of linear algebra through vectors and matrices. The subject develops linear systems and eigenvalues, then examines transformations and computational verification. Solve a small system using manual reasoning and a computational check.
- Discrete Mathematics. Study logic and sets as foundations for discrete mathematics. The subject develops relations and combinatorics, then examines graph theory and proof methods. Solve discrete problems and relate them to a computing application.
- Applied Statistics. Examine descriptive measures, probability and their relationship within applied statistics. The subject develops sampling and estimation, then examines hypothesis testing and regression. Analyse an approved dataset and distinguish uncertainty from systematic bias.
- Programming Fundamentals. Develop your understanding of programming fundamentals through variables and control flow. The subject develops functions and data structures, then examines file handling and testing. Develop a small program which processes a defined teaching dataset.
- Computer Architecture. Study processor organisation and instruction execution as foundations for computer architecture. The subject develops memory hierarchy and input and output, then examines parallelism and performance. Compare architectural choices using a simulator and controlled workloads.
- Academic and Professional Communication. Examine reading strategies, evidence use and their relationship within academic and professional communication. The subject develops report structure and presentations, then examines audience and referencing. Prepare a report and presentation for a defined professional audience.
- Digital Information Skills. Develop your understanding of digital information skills through file organisation and word processing. The subject develops spreadsheets and online information, then examines account security and data integrity. Organise files and analyse a small dataset using common digital tools.
Stage 2: Core computing and data
- Algorithms and Data Structures. Study complexity and lists and trees as foundations for algorithms and data structures. The subject develops graphs and searching, then examines sorting and algorithm comparison. Implement and compare algorithms on controlled test inputs.
- Database Systems. Examine data models, relational design and their relationship within database systems. The subject develops queries and transactions, then examines data integrity and access control. Build a database for synthetic organisational records.
- Software Engineering. Develop your understanding of software engineering through requirements and architecture. The subject develops version control and testing, then examines collaboration and maintenance. Develop a small application through documented review and testing.
- Operating Systems. Study processes and memory as foundations for operating systems. The subject develops file systems and scheduling, then examines concurrency and system administration. Investigate an authorised virtual-machine environment and record system behaviour.
- Computer Networks. Examine layered communication, addressing and their relationship within computer networks. The subject develops routing and switching, then examines network services and troubleshooting. Configure a small isolated teaching network or simulation.
- Data Preparation and Exploration. Develop your understanding of data preparation and exploration through data provenance and cleaning. The subject develops missing values and transformation, then examines visual exploration and reproducibility. Prepare a documented dataset while retaining an audit of changes.
- Probability and Statistical Modelling. Study random variables and distributions as foundations for 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.
- Artificial Intelligence Foundations. Examine search, problem representation and their relationship within artificial intelligence foundations. The subject develops heuristics and planning, then examines intelligent agents and evaluation. Implement a small problem-solving agent in a teaching simulation.
Stage 3: AI and machine learning
- Machine Learning. Develop your understanding of machine learning through learning tasks and features. The subject develops training and generalisation, then examines model selection and error analysis. Compare a baseline and candidate model on appropriately separated data.
- Deep Learning. Study neural representations and optimisation as foundations for deep learning. The subject develops regularisation and model architectures, then examines training diagnostics and evaluation. Investigate a small model using an approved public dataset.
- Knowledge Representation and Reasoning. Examine logic, rules and their relationship within knowledge representation and reasoning. The subject develops ontologies and inference, then examines uncertainty and knowledge quality. Build a small knowledge model and test it against defined questions.
- Natural Language Processing. Develop your understanding of natural language processing through text representation and linguistic variation. The subject develops language modelling and classification, then examines translation evaluation and language-data ethics. Analyse an authorised language dataset, including relevant regional variation.
- Computer Vision. Study image representation and features as foundations for computer vision. The subject develops recognition and detection, then examines image evaluation and dataset bias. Compare image-analysis methods on an authorised teaching dataset.
- Optimisation Methods. Examine objective functions, constraints and their relationship within optimisation methods. The subject develops gradient methods and linear programming, then examines convexity concepts and sensitivity. Solve a constrained allocation or model-fitting problem.
- Reinforcement Learning. Develop your understanding of reinforcement learning through states and actions and rewards. The subject develops policies and value functions, then examines exploration and evaluation limits. Study a small simulated decision task with no real-world control.
- Responsible AI and Data Governance. Study consent and privacy as foundations for responsible ai and data governance. The subject develops bias and explainability, then examines human oversight and deployment accountability. Audit a proposed AI use case for data and decision risks.
Stage 4: Integration and research
- Data Engineering. Examine data pipelines, storage design and their relationship within data engineering. The subject develops transformation and batch processing, then examines data validation and pipeline monitoring. Build a reproducible pipeline using synthetic or permissioned data.
- Research Methods. Develop your understanding of research methods through research questions and literature review. The subject develops study design and data collection, then examines ethics and interpretation. Prepare a feasible investigation proposal with a defined evidence need.
- Project Development and Feasibility. Study problem definition and evidence review as foundations for project development and feasibility. The subject develops requirements and method selection, then examines feasibility and evaluation criteria. Develop an approved discipline-specific project proposal with a supervisor.
- Integrated Project and Technical Report. Examine implementation, evidence collection and their relationship within integrated project and technical report. The subject develops analysis and verification, then examines limitations and communication. Complete the approved project and maintain an auditable evidence record.
Assessment and practical learning
- Proposed assessment: concept tests, data-analysis exercises and evidence-based case reports using Southern African examples.
- Practical observations, field or laboratory records and an individual explanation of methods, uncertainty and results.
- An applied investigation with a report and presentation. Assessment weighting and progression remain subject to BMIT approval.
Practical application
Practical work requires reliable computing access, versioned code and documented datasets. Learners should compare baseline and candidate models on appropriately separated data. Projects involving personal information require permission and privacy controls. Claims about model performance should include error analysis and limits of generalisation.
Subject descriptions
Select a subject to read its learning outcomes, main topics, practical task and assessment.
Stage 1: Mathematical and computing foundations
Differential Calculus
Examine functions, limits and their relationship within differential calculus. The subject develops differentiation and rates of change, then examines optimisation and model assumptions. Analyse a measured change and compare graphical and algebraic solutions.
Learning outcomes
- Explain functions and limits using an appropriate example.
- Analyse a subject-related problem involving differentiation and rates of change.
- Present reasoned evidence addressing optimisation and model assumptions.
Main topics
- Functions
- Limits
- Differentiation
- Rates of change
- Optimisation
- Model assumptions
Practical task
Analyse a measured change and compare graphical and algebraic solutions.
Assessment
Submit worked problems and a short interpretation of an optimisation case.
Linear Algebra
Develop your understanding of linear algebra through vectors and matrices. The subject develops linear systems and eigenvalues, then examines transformations and computational verification. Solve a small system using manual reasoning and a computational check.
Learning outcomes
- Explain vectors and matrices using an appropriate example.
- Analyse a subject-related problem involving linear systems and eigenvalues.
- Present reasoned evidence addressing transformations and computational verification.
Main topics
- Vectors
- Matrices
- Linear systems
- Eigenvalues
- Transformations
- Computational verification
Practical task
Solve a small system using manual reasoning and a computational check.
Assessment
Submit calculations and an explanation of the meaning of the solution.
Discrete Mathematics
Study logic and sets as foundations for discrete mathematics. The subject develops relations and combinatorics, then examines graph theory and proof methods. Solve discrete problems and relate them to a computing application.
Learning outcomes
- Explain logic and sets using an appropriate example.
- Analyse a subject-related problem involving relations and combinatorics.
- Present reasoned evidence addressing graph theory and proof methods.
Main topics
- Logic
- Sets
- Relations
- Combinatorics
- Graph theory
- Proof methods
Practical task
Solve discrete problems and relate them to a computing application.
Assessment
Submit proofs and an applied problem analysis.
Applied Statistics
Examine descriptive measures, probability and their relationship within applied statistics. The subject develops sampling and estimation, then examines hypothesis testing and regression. Analyse an approved dataset and distinguish uncertainty from systematic bias.
Learning outcomes
- Explain descriptive measures and probability using an appropriate example.
- Analyse a subject-related problem involving sampling and estimation.
- Present reasoned evidence addressing hypothesis testing and regression.
Main topics
- Descriptive measures
- Probability
- Sampling
- Estimation
- Hypothesis testing
- Regression
Practical task
Analyse an approved dataset and distinguish uncertainty from systematic bias.
Assessment
Submit a reproducible analysis with justified methods and interpretation.
Programming Fundamentals
Develop your understanding of programming fundamentals through variables and control flow. The subject develops functions and data structures, then examines file handling and testing. Develop a small program which processes a defined teaching dataset.
Learning outcomes
- Explain variables and control flow using an appropriate example.
- Analyse a subject-related problem involving functions and data structures.
- Present reasoned evidence addressing file handling and testing.
Main topics
- Variables
- Control flow
- Functions
- Data structures
- File handling
- Testing
Practical task
Develop a small program which processes a defined teaching dataset.
Assessment
Submit working code, tests and a concise user explanation.
Computer Architecture
Study processor organisation and instruction execution as foundations for computer architecture. The subject develops memory hierarchy and input and output, then examines parallelism and performance. Compare architectural choices using a simulator and controlled workloads.
Learning outcomes
- Explain processor organisation and instruction execution using an appropriate example.
- Analyse a subject-related problem involving memory hierarchy and input and output.
- Present reasoned evidence addressing parallelism and performance.
Main topics
- Processor organisation
- Instruction execution
- Memory hierarchy
- Input and output
- Parallelism
- Performance
Practical task
Compare architectural choices using a simulator and controlled workloads.
Assessment
Submit a performance report with reproducible measurements.
Academic and Professional Communication
Examine reading strategies, evidence use and their relationship within academic and professional communication. The subject develops report structure and presentations, then examines audience and referencing. Prepare a report and presentation for a defined professional audience.
Learning outcomes
- Explain reading strategies and evidence use using an appropriate example.
- Analyse a subject-related problem involving report structure and presentations.
- Present reasoned evidence addressing audience and referencing.
Main topics
- Reading strategies
- Evidence use
- Report structure
- Presentations
- Audience
- Referencing
Practical task
Prepare a report and presentation for a defined professional audience.
Assessment
Submit revised written work and an individual presentation.
Digital Information Skills
Develop your understanding of digital information skills through file organisation and word processing. The subject develops spreadsheets and online information, then examines account security and data integrity. Organise files and analyse a small dataset using common digital tools.
Learning outcomes
- Explain file organisation and word processing using an appropriate example.
- Analyse a subject-related problem involving spreadsheets and online information.
- Present reasoned evidence addressing account security and data integrity.
Main topics
- File organisation
- Word processing
- Spreadsheets
- Online information
- Account security
- Data integrity
Practical task
Organise files and analyse a small dataset using common digital tools.
Assessment
Submit a digital portfolio demonstrating accurate and responsible work.
Stage 2: Core computing and data
Algorithms and Data Structures
Study complexity and lists and trees as foundations for algorithms and data structures. The subject develops graphs and searching, then examines sorting and algorithm comparison. Implement and compare algorithms on controlled test inputs.
Learning outcomes
- Explain complexity and lists and trees using an appropriate example.
- Analyse a subject-related problem involving graphs and searching.
- Present reasoned evidence addressing sorting and algorithm comparison.
Main topics
- Complexity
- Lists and trees
- Graphs
- Searching
- Sorting
- Algorithm comparison
Practical task
Implement and compare algorithms on controlled test inputs.
Assessment
Submit code, tests and a complexity discussion.
Database Systems
Examine data models, relational design and their relationship within database systems. The subject develops queries and transactions, then examines data integrity and access control. Build a database for synthetic organisational records.
Learning outcomes
- Explain data models and relational design using an appropriate example.
- Analyse a subject-related problem involving queries and transactions.
- Present reasoned evidence addressing data integrity and access control.
Main topics
- Data models
- Relational design
- Queries
- Transactions
- Data integrity
- Access control
Practical task
Build a database for synthetic organisational records.
Assessment
Submit the schema, queries, test data and integrity checks.
Software Engineering
Develop your understanding of software engineering through requirements and architecture. The subject develops version control and testing, then examines collaboration and maintenance. Develop a small application through documented review and testing.
Learning outcomes
- Explain requirements and architecture using an appropriate example.
- Analyse a subject-related problem involving version control and testing.
- Present reasoned evidence addressing collaboration and maintenance.
Main topics
- Requirements
- Architecture
- Version control
- Testing
- Collaboration
- Maintenance
Practical task
Develop a small application through documented review and testing.
Assessment
Submit software, a test report and design documentation.
Operating Systems
Study processes and memory as foundations for operating systems. The subject develops file systems and scheduling, then examines concurrency and system administration. Investigate an authorised virtual-machine environment and record system behaviour.
Learning outcomes
- Explain processes and memory using an appropriate example.
- Analyse a subject-related problem involving file systems and scheduling.
- Present reasoned evidence addressing concurrency and system administration.
Main topics
- Processes
- Memory
- File systems
- Scheduling
- Concurrency
- System administration
Practical task
Investigate an authorised virtual-machine environment and record system behaviour.
Assessment
Submit practical records and an operating-system analysis.
Computer Networks
Examine layered communication, addressing and their relationship within computer networks. The subject develops routing and switching, then examines network services and troubleshooting. Configure a small isolated teaching network or simulation.
Learning outcomes
- Explain layered communication and addressing using an appropriate example.
- Analyse a subject-related problem involving routing and switching.
- Present reasoned evidence addressing network services and troubleshooting.
Main topics
- Layered communication
- Addressing
- Routing
- Switching
- Network services
- Troubleshooting
Practical task
Configure a small isolated teaching network or simulation.
Assessment
Submit diagrams, configurations and troubleshooting evidence.
Data Preparation and Exploration
Develop your understanding of data preparation and exploration through data provenance and cleaning. The subject develops missing values and transformation, then examines visual exploration and reproducibility. Prepare a documented dataset while retaining an audit of changes.
Learning outcomes
- Explain data provenance and cleaning using an appropriate example.
- Analyse a subject-related problem involving missing values and transformation.
- Present reasoned evidence addressing visual exploration and reproducibility.
Main topics
- Data provenance
- Cleaning
- Missing values
- Transformation
- Visual exploration
- Reproducibility
Practical task
Prepare a documented dataset while retaining an audit of changes.
Assessment
Submit the cleaned dataset, data dictionary and reproducible workflow.
Probability and Statistical Modelling
Study random variables and distributions as foundations for 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.
Artificial Intelligence Foundations
Examine search, problem representation and their relationship within artificial intelligence foundations. The subject develops heuristics and planning, then examines intelligent agents and evaluation. Implement a small problem-solving agent in a teaching simulation.
Learning outcomes
- Explain search and problem representation using an appropriate example.
- Analyse a subject-related problem involving heuristics and planning.
- Present reasoned evidence addressing intelligent agents and evaluation.
Main topics
- Search
- Problem representation
- Heuristics
- Planning
- Intelligent agents
- Evaluation
Practical task
Implement a small problem-solving agent in a teaching simulation.
Assessment
Submit a representation rationale and performance evaluation.
Stage 3: AI and machine learning
Machine Learning
Develop your understanding of machine learning through learning tasks and features. 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
Study neural representations and optimisation as foundations for 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.
Knowledge Representation and Reasoning
Examine logic, rules and their relationship within knowledge representation and reasoning. 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
Develop your understanding of natural language processing through text representation and linguistic variation. 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
Study image representation and features as foundations for 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.
Optimisation Methods
Examine objective functions, constraints and their relationship within optimisation methods. 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.
Reinforcement Learning
Develop your understanding of reinforcement learning through states and actions and rewards. The subject develops policies and value functions, then examines exploration and evaluation limits. Study a small simulated decision task with no real-world control.
Learning outcomes
- Explain states and actions and rewards using an appropriate example.
- Analyse a subject-related problem involving policies and value functions.
- Present reasoned evidence addressing exploration and evaluation limits.
Main topics
- States and actions
- Rewards
- Policies
- Value functions
- Exploration
- Evaluation limits
Practical task
Study a small simulated decision task with no real-world control.
Assessment
Submit a policy comparison and discussion of reward-design limitations.
Responsible AI and Data Governance
Study consent and privacy as foundations for responsible ai and data governance. 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.
Stage 4: Integration and research
Data Engineering
Examine data pipelines, storage design and their relationship within data engineering. The subject develops transformation and batch processing, then examines data validation and pipeline monitoring. Build a reproducible pipeline using synthetic or permissioned data.
Learning outcomes
- Explain data pipelines and storage design using an appropriate example.
- Analyse a subject-related problem involving transformation and batch processing.
- Present reasoned evidence addressing data validation and pipeline monitoring.
Main topics
- Data pipelines
- Storage design
- Transformation
- Batch processing
- Data validation
- Pipeline monitoring
Practical task
Build a reproducible pipeline using synthetic or permissioned data.
Assessment
Submit the pipeline, validation checks and operating notes.
Research Methods
Develop your understanding of research methods through research questions and literature review. The subject develops study design and data collection, then examines ethics and interpretation. Prepare a feasible investigation proposal with a defined evidence need.
Learning outcomes
- Explain research questions and literature review using an appropriate example.
- Analyse a subject-related problem involving study design and data collection.
- Present reasoned evidence addressing ethics and interpretation.
Main topics
- Research questions
- Literature review
- Study design
- Data collection
- Ethics
- Interpretation
Practical task
Prepare a feasible investigation proposal with a defined evidence need.
Assessment
Submit a proposal, methods rationale and ethics considerations.
Project Development and Feasibility
Study problem definition and evidence review as foundations for project development and feasibility. The subject develops requirements and method selection, then examines feasibility and evaluation criteria. Develop an approved discipline-specific project proposal with a supervisor.
Learning outcomes
- Explain problem definition and evidence review using an appropriate example.
- Analyse a subject-related problem involving requirements and method selection.
- Present reasoned evidence addressing feasibility and evaluation criteria.
Main topics
- Problem definition
- Evidence review
- Requirements
- Method selection
- Feasibility
- Evaluation criteria
Practical task
Develop an approved discipline-specific project proposal with a supervisor.
Assessment
Submit a proposal, evidence review and evaluation plan.
Integrated Project and Technical Report
Examine implementation, evidence collection and their relationship within integrated project and technical report. The subject develops analysis and verification, then examines limitations and communication. Complete the approved project and maintain an auditable evidence record.
Learning outcomes
- Explain implementation and evidence collection using an appropriate example.
- Analyse a subject-related problem involving analysis and verification.
- Present reasoned evidence addressing limitations and communication.
Main topics
- Implementation
- Evidence collection
- Analysis
- Verification
- Limitations
- Communication
Practical task
Complete the approved project and maintain an auditable evidence record.
Assessment
Submit the final project, report and individual oral defence.
Instructors
