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3. Design Artificial Intelligence technology to be more responsible and in line with the needs of
industry and society

Synopsis of Course Content
The course describes the concepts and philosophy of data privacy and ethics in Artificial Intelligence
technologies. All strategies for developing a more responsible Artificial Intelligence system will be
explained in more detail. The course also analyse and critique issues of data privacy violations or
unethical values in current smart systems and technologies

Evaluation and Weightage
Continuous Assessment : 70%
Final Examination : 30%

WQF7001 Artificial Intelligence Research Project

Learning Outcomes
At the end of this course, students are able to:
1. Design solution using artificial intelligence techniques for real world problems.
2. Develop Artificial Intelligence-based solution formulated on project objectives.
3. Explain solution in oral and written presentation related to artificial intelligence research.

Synopsis of Course Content
A research project is a medium-scale project to enable students to do research related to artificial
intelligence. Research projects allow students to use actual data from industry partners or public data
to create applications by applying knowledge in the basic, theories and scientific methods to solve
problems related to artificial intelligence. During the project, students will engage in the overall process
of general research, starting with identifying problems, collecting and processing data, recommending
solution methods, applying appropriate scientific methods and ending with implementing affordable
solutions and evaluations. At the end of the course, students are required to submit a project report and
perform a project presentation.

Evaluation and Weightage
Continuous Assessment : 100%
Final Examination : 0%

WQF7008 Practical Deep Learning

Learning Outcomes
At the end of the course, students are able to:

1. Unifies the knowledge on the fundamentals and architectures of deep learning, and the need for
parallel and distributed computing for deep learning.
2. Integrate and develop the requirements for cloud computing infrastructure, GPU and relevant
software as well as tools for setting up, modelling, debugging and serving of deep learning
projects.
3. Practise the knowledge and skills to design deep learning based solutions.