MSc Advanced Computer Science with Artificial Intelligence

This module aims to ensure that you're aware of the legal, social, ethical and professional issues commensurate with the practice of Information Systems Engineering. 

By the end of the module you should understand:

• classifications of problem features for autonomous systems
• common approaches to deliberate reasoning for intelligent agents
• the challenges in integrated planning and execution, and some approaches to solving them
• how to build systems in the Robot Operating System

By the end of the module you should be able to:

• understand the challenges in the training of deep neural network and how to overcome the challenges with few shot learning techniques
• understand how to process sequential data with deep neural network and the applications in natural language processing
• develop understanding of the potentials and generalisation of deep neural networks through theoretical analysis
• understand deep generative models and their main approaches
• understand how to build Deep Neural Networks in Python using packages such as Keras/PyTorch and implement such networks

By the end of this module you should:

• understand many statistical techniques used in modern AI: basic data description (EDA), significance tests, statistical distributions, 'classical' and Bayesian inference, etc
• understand and be able to apply probability theory to common problems in modern AI: randomness, probability distributions, variance and expectation, expected values, etc
• appreciate how techniques from linear algebra are used in modern AI: scalars, vectors, matrices, tensors

This module aims to: 

• make you aware of key aspects of current software engineering research 
• familiarise you with the state-of-the-art in terms of what problems can be solved and what are the current exciting challenges 
• equip you with the necessary skills to allow you to contribute to the software engineering research community 
• equip you with the skills and background to appreciate the contributions to software engineering research across the full range of material presented at the key international conferences in the field 

The objectives of the module are to:

• understand challenges posed by big data, as they refer to its modelling, storage, and access
• be familiar with a number of different big data technologies and techniques, in terms of efficiency and scalability while also addressing design trade-offs and their impacts
• be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability while also addressing design trade-offs and their impacts
• be familiar with the Map-Reduce programming paradigm

You'll learn to:

• critically examine a number of influential information-seeking models
• provide an understanding of research methodologies for studying human information behaviour
• examine important concepts, such as relevance, in the context of information seeking and retrieval
• examine how findings from information seeking theory and practise can inform the design of information access systems
• outline the theory and technology used to construct modern Information Retrieval and Access systems
• critically evaluate the assumptions behind the evaluation of Information Retrieval systems

This module aims to provide you with an understanding of both quantitative and qualitative research processes and associated techniques, including the effective presentation of findings in accordance with the best principles of scholarship.

This module will cover an in-depth application of neural networks including convolutional neural networks (CNN), recurrent neural networks (RNN) and deep neural networks. Using these methods, the module will also explore topics such as visualisation of features, model explainability, object detection, image segmentation and image generation.

This module consists of about two-thirds game theory and about one-third reinforcement learning, although the two topics blend into each other. We'll also touch on some other points of contact between game theory and AI.

By the end of the module you should be able to:

• appreciate and explain the problems associated with mobile software environments
• identify and explain the models and techniques typically employed in the design and development of a range of software for mobile environments, and appreciate the limitations of these
• appreciate the role and impact of context-awareness and persuasion in modern mobile applications
• demonstrate the ability to implement selections from a range of the software typically used in mobile environments

This module will give you an extended understanding of the deep, technical issues underlying information systems, in the particular context of distributing content over the world-wide web.

This class provides an overview of the application of AI techniques including those which mimic natural evolutionary processes (genetic algorithms and genetic programming in particular) to a range of financial applications such as forecasting, portfolio optimisation and algorithmic trading.

After completing this module you'll be able to:

• understand the aims and fundamental principles of machine learning;
• understand a range of the essential core algorithms and approaches to machine learning;
• apply the algorithms covered on substantial data sets using Python and Scikit-learn and interpret the outcomes;
• understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations.

This module aims to provide tools and techniques for the effective analysis and design of business information systems and enable you to develop an understanding of their respective advantages, disadvantages and applicability.

On completion of this module you'll:

• gain an understanding of a range of evolutionary computational and machine learning techniques
• gain an understanding of the relative advantages and disadvantages of each technique for different financial applications
• be able to evaluate the results of a financial problem investigated using evolutionary computation and machine learning techniques