Page 110 - PG-Handbook23-24-finale

WOX7001 Research Methodology

Course Learning Outcomes
At the end of the course, students are able to:
1. Describe appropriate methodologies used in computer science and information technology
research.
2. Devise a plan to be carried out within a feasible duration for answering research problems and
questions identified.
3. Demonstrate attitude and character in line with professional and ethical codes in computer science
and information technology research.

Synopsis of Course Content
This course gives on overview of the dimensions of research in computer science and information
technology. Major considerations and tasks in conducting research in the areas such as review of
literature, identify problem statement, formulate research questions and objectives, select an
appropriate approach or method to the research, plan and manage the research, tools for research,
data analysis, and writing and presentation strategies, will be discussed too.
Evaluation and Weightage
Continuous Assessment : 100%
Final Examination : 0%

WOA7015 Advanced Machine Learning

Course Learning Outcomes
At the end of this course, students are able to:
1. Practice concepts and techniques for machine learning related to digital and numerical methods.
2. Report the solution to machine learning problems by devising and listing the steps in machine
learning applied to solve different types of problems
3. Demonstrate skills and knowledge on machine learning by managing a machine learning project.

Synopsis of Course Content
This course introduces advanced concepts and techniques for machine learning. It covers topics such
as linear and logistic regression, decision tree, neural network, and support vector machines as well as
reinforcement learning

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

WQF7002 Artificial Intelligence Techniques

Course Learning Outcomes
At the end of the course, students are able to:
1. Explain what constitutes Artificial Intelligence and identifying systems with Artificial Intelligence
elements.
2. Analyse the applications of Artificial Intelligence techniques in intelligent agents, expert systems,
artificial neural networks, and other machine learning models.
3. Apply basic principles of Artificial Intelligence in problem solving, inference, perception, knowledge
representation, and machine learning