(Award available for year: Master of Science)
Upon successful completion of the programme, students will be able to:
1. Integrate and apply advanced knowledge of machine learning, deep learning, and reinforcement learning to design and implement intelligent systems that learn from complex, multimodal data.
2. Critically analyse and evaluate the theoretical, mathematical, and computational principles that underpin modern artificial intelligence, demonstrating fluency in abstraction, modelling, and algorithmic reasoning.
3. Operationalise AI models within production-oriented workflows by applying best practices in MLOps, including reproducibility, scalability, and system monitoring.
4. Investigate and address issues of bias, alignment, accountability, and ethical deployment in the design and implementation of AI systems.
5. Synthesise and extend existing methods through independent research or experimentation that contributes to the development or critical evaluation of AI technologies.
6. Communicate and justify complex ideas, methodologies, and outcomes to both technical and non-technical audiences, demonstrating professional integrity, reflective awareness, and autonomy appropriate to advanced AI practice.
Upon successful completion of the programme, students will be able to:
1. Apply advanced analytical and problem-solving skills to design, implement, and evaluate intelligent systems that address complex and uncertain real-world challenges.
2. Demonstrate autonomy and adaptability in selecting, learning, and integrating emerging methods, frameworks, or technologies in response to evolving AI practice.
3. Communicate complex technical and analytical ideas clearly and persuasively to diverse audiences through written, visual, and oral forms.
4. Apply integrated problem-solving and systems thinking to design, evaluate, and refine complex AI solutions that align data, models, and ethical considerations across diverse application domains.
5. Exercise reflective and critical practice to evaluate personal and team performance, optimise processes, and continuously improve methodological rigour.
6. Manage projects and workflows systematically, balancing technical innovation, ethical awareness, and professional accountability in the design and deployment of AI solutions.
The summative assessments take usually the form of:
1. One Test assessment in the middle of the module with relatively lighter weight between 20-40%
2. Final Project Assessment at the end of the module with a higher weight of 80-60%.
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