Diploma in Artificial Intelligence

Admission Requirements

• Typical Age: 18+
• Entry Point: Learners enrol at Level 4. Completing Level 4 then meets the entry requirement for the Level 5 stage.
• Prior Qualifications: A GCE Advanced level profile with achievement in 2 or more subjects, a Level 3 qualification in Data Science or a related field, or an Access to Higher Education Certificate from an approved institution.
• International Qualifications: Equivalent qualifications may include:
  • High school diplomas or equivalent secondary qualifications in Asia, Africa, Europe, and the Americas.
• English Proficiency (for non-native speakers): IELTS 5.5, CEFR level B2, or equivalent.
• Additional Guidance: For assistance with qualification equivalency or language requirements, please contact our admissions office.

Structure of the Qualification

• Total Credits: 240 ECTS (120 ECTS at Level 4 and 120 ECTS at Level 5)
• Modules: 12 mandatory modules, six at each level, each worth 20 credits.

Level 4 Core Modules

1. Introduction to Artificial Intelligence and Applications (20 Credits): Introduces AI principles, models, technologies, and industry applications, including how AI can address future sustainability needs.
2. Mathematical Foundations for Machine Learning (20 Credits): Covers probability, statistics, hypothesis testing, regression, and calculus, with application to business problems.
3. Data Science Using Python (20 Credits): Teaches Python and key libraries for data manipulation, visualisation, exploratory data analysis, and basic machine learning.
4. Big Data Management (20 Credits): Explores big data technologies such as Hadoop and NoSQL, along with data mining, visualisation, industry applications, and ethical implications.
5. Introduction to Deep Learning (20 Credits): Explains neural networks and key deep learning concepts, including applying and evaluating models for image classification.
6. Artificial Intelligence Ethics (20 Credits): Examines ethical frameworks, bias, privacy, security, governance, and global approaches to AI regulation.

Level 5 Core Modules

1. Visualisation (20 Credits): Develops practical skills in data preparation, visual communication, and interpreting business insights using tools such as Tableau and Power BI.
2. Reinforced Machine Learning (20 Credits): Covers reinforcement learning principles, algorithms, libraries, and frameworks, along with model evaluation and tuning.
3. Natural Language Processing (20 Credits): Explores text preprocessing, key NLP libraries and methods, and sentiment analysis in business scenarios.
4. Human-AI Interaction (20 Credits): Examines user-centred design, human factors, multimodal interaction, explainable AI, and collaboration between people and AI systems.
5. Advanced Deep Learning (20 Credits): Builds on Level 4 with advanced neural network architectures, transfer learning, hyperparameter tuning, generative models, and applications in NLP and reinforcement learning.
6. Introduction to Computer Vision (20 Credits): Introduces image processing, feature extraction, object detection, image segmentation, and deep learning models for computer vision.

Explore the complete curriculum for this course to gain a detailed understanding of the topics covered, learning objectives, and key skills you’ll develop. See the full curriculum below.

View Curriculum

Assessment and Regulations

Assessment Approach

Assessment is based on a combination of exams, assignments, projects, and presentations, aligned with each unit’s learning outcomes:

• Written Assignments: Assess comprehension and application of AI principles, using examples from the workplace, case studies, or research.
• Exams: Test learners’ understanding of key AI concepts.
• Presentations: Assess learners’ ability to communicate AI concepts, findings, and business insights effectively.
• Projects and Case Studies: Practical tasks that relate to real-world AI issues, requiring critical thinking and problem-solving.

Grading System

Grades are awarded based on the successful completion of unit-specific assessment criteria:

• Distinction: 70% and above
• Merit: 60%–69%
• Pass: 40%–59%
• Fail: 0%–39%

Completion Requirements

Learners must pass all units by meeting all required learning outcomes and assessment criteria. All assessments are marked internally by the approved centre and externally moderated by the Awarding Body to ensure quality and fairness.

Professional Development and Progression

Progress to Higher-Level Study:

• Progress to a Level 6 qualification in a relevant area.

Employment Opportunities:

This programme prepares learners for roles such as:

• AI Developer
• Machine Learning Engineer
• NLP Engineer
• Computer Vision Engineer
• Data Scientist

Work-Based Learning:

• Practical tasks and case studies within each module support skill development and readiness for the workplace. Where possible, learners are encouraged to draw on workplace opportunities for research and investigation.

When you enrol in our Level 4+5 bundle, you’ll start on the same course pathway for both levels. If you wish to change your Level 5 route after registration, just reach out to Student Services—we’re here to guide you toward the best fit.