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WQF7003 Intelligent Computation

Learning Outcomes
At the end of the course, students are able to:
1. Explain how mathematical theories help in solving AI problems.
2. Solve AI problems with formal reasoning.
3. Combine mathematical techniques in solving artificial intelligence problems.

Synopsis of Course Content
This course covers fundamental mathematical theories that support the development of artificial
intelligence. Topics covered include logic and reasoning, linear algebra, graph theory and search
algorithms, and probability theory.

This course finds relation with other courses in the program, such as: Advanced Machine Learning
where linear algebra, graph theory and search algorithms are used heavily; Computer Vision and
Image Processing where linear algebra and probability theory finds their applications; and Natural
Language Processing which has relation with graph theory and search algorithms, as well as logic
and reasoning. The content of this course is also the fundamental of courses like Practical Deep
Learning and Artificial Intelligence Techniques.

Evaluation and Weightage
Continuous Assessment : 50%
Final Examination : 50%

WQF7004 Data Analytics in Artificial Intelligence

Learning Outcomes
At the end of this course, students are able to
1. Explain the basic concepts of data analytics in Artificial Intelligence in various domains.
2. Design domain-based data analytic pipeline to solve real world Artificial Intelligence problems.
3. Apply suitable data analytics techniques to solve real world problems for Artificial Intelligence.

Synopsis of Course Content
This course aims to develop students' ability to describe, explore and analyse various types of data
(tabular,text and images) using suitable data analytics techniques and do predictive modelling by
using different Machine Learning techniques.

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

WQF7005 Data Privacy and Artificial Intelligence Ethics

Learning Outcomes
At the end of this course, the students are able to:
1. Assess the importance of data privacy and ethical concepts in the development of Artificial
Intelligence system.
2. Check current smart systems and technologies that are less concerned with ethical issues and data
privacy.