Module Descriptors

Research Methods

This module develops students’ ability to conduct a research assignment related to their field of study and prepares them to carry out research projects successfully. The initial stages of the module will consider key issues related to research methods in general, types of research, and identifying research problems. Students will also learn to evaluate and criticize academic journal articles and conduct a comprehensive systematic review in a specific field of study. The module will then consider sampling and sampling methods, qualitative, quantitative, and mixed research methods. The module also considers descriptive and inferential statistics and sheds light on data analysis through the lens of structural equation modelling (SEM).

Advances in Artificial Intelligence

In-depth introduction to Artificial Intelligence focusing on techniques that allow intelligent systems to reason effectively with uncertain information and cope limited computational resources. Topics include: problem-solving using search, heuristic search techniques, constraint satisfaction, local search, abstraction and hierarchical search, resource-bounded search techniques, principles of knowledge representation and reasoning, logical inference, reasoning under uncertainty, belief networks, decision theoretic reasoning, planning under uncertainty using Markov decision processes, multi-agent planning, and computational models of bounded rationality.

Computer network and data security

This module provides students with an opportunity to gain an in depth understanding of the theories and issues on the network and data security. In addition to covering network and data security technologies, such as cryptography, operating systems security, malicious software’s, denial of services, and intrusion detection systems.

It cover network and data security is design in (firewalls , intrusion prevention system, malicious programs immune systems and Malicious Program Detection System (MPDS)), and analysed (access control, security issues and threats). Students will also learn about the analysis the main challenges faced when dealing with security assessment. Practical case studies will be used for illustration.

Advanced Algorithms

Algorithms address the problems of how to best solve specific problems using minimal time and space resources. The study of designing efficient algorithms is an important component and core module of computer science. Many CS researchers make contributions by designing advanced algorithms to solve real-world problems. Research in advanced algorithms anticipates the growing quantity and power of data and works to use algorithms to their full capacity. Some research areas include: understanding the complexity of computational problems, designing secure cryptographic systems, computational geometry, computational topology, etc. Students would eventually learn to design algorithms for their research studies.

Knowledge Management

The aim of this module is to teach the principles and technologies of knowledge management across various sectors. The module covers the fundamental concepts in the study of knowledge and its acquisition, representation, sharing, application, protection, and management. The focus is on methods, techniques, and tools for computer support of knowledge management, and how to apply a knowledge management system using one of the innovative knowledge-based system tools.

Advanced Software Engineering

The module exposes students to the fundamental key challenges, and new research needed for architecting the future of software engineering. Major topics covered are:

Introduction: Architecting the Future of Software Engineering; Envisioning the Future of Software Engineering

Research Focus Area 1: Advanced Development Paradigms

AI-Augmented Software Development; Assuring Continuously Evolving Software Systems; Software Construction through Compositional Correctness

Research Focus Area 2: Advanced Architectural Paradigms

Engineering AI-Enabled Software Systems; Engineering Socio-Technical Systems; Engineering Quantum Computing Software Systems; Responsible Software Engineering

Research Focus Area 3: Research Roadmap

Natural Language Processing

students will learn basic Natural Language Processing (NLP) methods (such as tokenization, lemmatization, stemming), basic representation methods (such as one-hot encoding, TF-IDF), as well as corpus-based techniques (such as word and sentence vectors, transformer language models). The module discusses methods and recent directions for researches in sentiment and emotion analysis in text, named entity recognition, machine translation, sequence to sequence learning, and among others.

Big Data and Analytics

This module provides an in depth understanding of the theories and issues on analytics and big data technologies, such as Map Reduce concepts, Hadoop and HDFS. The module covers how big data is collected, stored (Relational Algebra operators vs SQL syntax, Data Mining using SQL), and analysed (statistical, visualization, classification, and clustering techniques). Students will be exposed to special types of datasets, including graphs and time series.

Advanced Topics in Computer Science

This module provides students with an opportunity to gain an in depth understanding of the theories and issues on an advanced topic in CS. The course should cover new technologies that are not offered in the current modules’ descriptions (e.g Energy Aware Computing, Bioinformatics, Health Informatics etc.)

Thesis

This element of the programme comprises the planning, development and submission of a doctoral research thesis of 60,000-80,000 words. The student will carry out a major research investigation. The student will work under the supervision of a Director of Studies and second supervisor from BUiD and an external academic advisor. The PhD thesis will be expected to make a distinct and original contribution to the knowledge of the topic addressed.