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Synopsis of Course Content
This course is closely linked with the Advance Machine Learning course which is a pre-requisite for this
course. It reinforces the knowledge on the fundamental concepts related to deep learning (such as
different deep learning architectures) and introduces practical techniques to get started on Artificial
Intelligence projects and develop an industry portfolio. Specifically, it will provide the necessary
knowledge and skills on how to design a deep learning production system end-to-end: project scoping,
data needs, modelling strategies, and system deployment requirements.

Evaluation and Weightage
Continuous Assessment : 60%
Final Examination : 40%

WQF7009 Explainable Artificial Intelligence (XAI)

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

1. Categorize the concepts of Explainable Artificial Intelligence (AI) and the current techniques for
generating explanations from black-box machine learning methods.
2. Design the Explainable AI methods.
3. Develop the ability to critically assess the state-of-the-art of Explainable AI methods.

Synopsis of Course Content
This course gives an introduction to Explainable AI (XAI), providing an overview of relevant concepts
such as interpretability, transparency and black-box machine learning methods. The course provides
an overview of state-of-the-art methods for generating explanations, and touches upon issues related
to decision-support, human interaction with AI/intelligent systems and their evaluation. In summary, the
Explainable AI course covers the following topics: definitions and concepts such as black-box models,
transparency, interpretable machine learning and explanations, explainable AI models, methods for
Explainable AI, applications and examples.
Evaluation and Weightage
Continuous Assessment : 60%
Final Examination : 40%

WQF7010 Robotics and Automation

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

1. Design robotic and automation systems using parts like sensors, controllers and actuators.
2. Infer patterns from data collected.
3. Evaluate robotic and automation systems for optimum performance in various applications.

Synopsis of Course Content
This course focuses on developing robotic and automation systems by integrating components such as
sensors, controllers, motors and actuators. Students apply data acquisition methods, control methods
and also program robot sensing, connectivity, mobility and manipulation to achieve automation.
Additionally, students can apply artificial intelligence techniques to analyse collected data for informed
decision making.

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