MSc Advanced Computer Science

This module develops the theoretical and practical knowledge of design and development of distributed systems that operate on various devices, from cloud services to servers to smartphones. The module presents concepts of models of distributed systems, inter-process communication and synchronisation, load balancing, replication and consistency, middleware and security implications, emerging trends and challenges.

Topics include asynchronous synchronous computation and/or communication, concurrency control, robustness and fault tolerance, general purpose programming with GPU, heterogeneity, interconnection topologies, load balancing, memory consistency models, memory hierarchies, message passing interface (MPI), MIMD/SIMD, multithreaded programming, parallel algorithms & architectures, performance analysis and tuning, shared or distributed memory models, scalability and scheduling. Contemporary tools such as nVidia CUDA, Amazon AWS, Microsoft Azure, OpenStack, OpenMP and Hadoop etc. are also discussed.

Having studied core Computer Science topics, students have the opportunity to apply a range of conceptual knowledge and practical implementation tools to an in-depth development of a real-world project of their particular interest. The aim is to develop the skills expected at postgraduate level and equip Computer Science students with imperative knowledge, research & analysis skills, application of software development life cycle and critical insights into the process of transforming user requirements into practical software solutions.

This module develops the technical perspectives and practical knowledge of computer vision and its applications. Evolving as a confluence of image processing, artificial intelligence and machine learning, this module incorporates low- and high-level feature extraction from images and videos, implementation of statistical pattern recognition and generation of predictions and semantic analyses.

Topics include image characterises, processing in spatial and frequency domains, linear transformations, wavelet decomposition, feature detection and extraction, image registration, segmentation, motion estimation, probabilistic models of object detection and recognition, object tracking, scene labelling and context and scene understanding.

The practical implementation of state-of-the-art algorithms is done using Python (or Matlab) environment with relevant libraries such as OpenCV.

This module introduces objectives and importance of research in Computer Science, systematic literature review, problem statement and hypothesis formulation, experiment design, identifying types of variables and data wrangling, sampling techniques, quantitative and qualitative research, mixed methods of research, data imputation, types of statistical tests and evaluation measures. The module also discusses ethical constraints, intellectual property rights and legal requirements. The students are expected to conduct data analyses and present reports in a variety of formats and visualizations.

This module develops the technical perspectives and practical knowledge of mobile and ubiquitous computing systems. People continuously use various devices, from cloud services to servers to smartphones, without being actively aware of their interaction with computer systems in everyday life. Ubiquitous (Pervasive) computing integrates connectivity functionalities into all of the objects in our environment for seamless interaction and automation.

The module presents concepts of advanced human-computer interaction that address cutting edge real-world problems, interaction paradigms, cognitive models, practices of developing smooth and seamless interactions, usability engineering and inspection, enhancing user experience within the subjective and objective requirements, wearable systems, activity tracking, smart homes, edge computing and discussion of emerging trends and research challenges. Furthermore, students learn concepts such as development activities, resource management within mobile devices, media services and content provision, data storage, security, managing evolution of tablets and phablets, speech sensors networking, concurrent processes and threads, deployment strategies, integration of AI on supported hardware platforms and cognitive mobile analytics.

Students will critically evaluate different architectures and frameworks for designing and developing software applications in application domains such as logistics, autonomous vehicles, home automation, e-commerce or healthcare etc. Emphasis on improving user experience, resource management, portability and interoperability. Practical programming will be done using MS Visual Studio, Eclipse, Android Studio, Xcode or similar suite.

This module develops the theoretical, mathematical and practical foundations of algorithms in Computer Science. The time and space trade-offs and their relation to size and nature of inputs are fundamental in all software applications of programming, databases and distributed computing, machine learning, computer vision, deep learning, natural language processing, big data analytics, cryptography and information retrieval etc.

The module aims to introduce students to an in-depth understanding of ‘best’, ‘average’ and ‘worst’ case scenarios, iteration versus recursion, backtracking, linear versus dynamic programming, greedy algorithms, self-balancing trees, topological graphs (directed and undirected, traversals, colouring, distance algorithms, spanning trees), sorting, hashing and searching algorithms. Topics covered include analysis on nature and size of inputs, asymptotic notations (Big-O, Big Ω, Big Θ, little-o, little-ω), recursion and recurrence relations, design of algorithms: brute force, divide and conquer and greedy approach, dynamic programming; elements of dynamic programming, search trees; heaps; hashing; graph algorithms, shortest paths, sparse graphs, string matching, polynomial and matrix calculations and complexity classes.

A variety of algorithms are practically implemented using Python programming language (with open-source libraries) so that upon completion of the module, students should be able to critically explain the mathematical concepts and apply algorithms appropriate to a particular situation.

This module develops the technical perspectives and practical knowledge of information security, vulnerabilities and risks related to web applications, networks, software and mobile applications, approaches of developing and deploying effective countermeasures and ethical and legal considerations.

Topics include security design principles, security mechanisms, symmetric and asymmetric cryptography, encryption, hash functions, digital signatures, key management, stream and block ciphers, DES, message authentication, RSA, authentication and access control, software security, vulnerabilities and protections, malware detection, firewalls, NIST cyber security framework, intrusion detection, security policy formation and enforcement, risk assessment, cybercrime, laws and ethics in information security.