Applied Artificial Intelligence (AI) with Healthcare Innovation MSc

This module entails the theoretical knowledge and practical skills of wired and wireless computer networks, Internet of Things and cyber security. Designed to introduce advanced communication concepts to both networking experts and non-experts, the module aims to enable students to design, develop, implement and secure networked systems.

This module develops the theoretical foundation and practical skills of artificial intelligence (AI) and data science (DS) and their applicability in real-world scenarios. Building upon the statistical and mathematical underpinnings, this module aims to teach students the established approaches, emerging trends and challenges in classification, regression and clustering tasks. A variety of machine learning approaches used in AI and DS applications are introduced and Python programming language (with open-source libraries) is suggested for developing practical solutions.

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 will provide students opportunity to understand and apply computational techniques to analyse and synthesize natural language and speech – Natural Language Processing (NLP). An interdisciplinary bridging of linguistics, information retrieval and machine learning will provide necessary skills to develop applications capable of comprehending, manipulating and generating natural language text and speech similar to Large Language Models.

This module will introduce topics in NLP including tokenization, stemming, parsing, lemmatization, basic text processing, linguistics and NLP tasks, Python NLTK library for NLP, text preprocessing and n-grams, Softmax / MAXENT (sequence) classifiers, sequence

classifiers for POS and NER, Deep learning-based word representations & deep networks

for NER, recurrent networks and language modelling, statistical machine

translation, word alignment, parallel corpora, decoding, evaluation, modern deep learning machine translation systems (phrase-based, syntactic), syntax and parsing, co-reference resolution, tree recursive neural networks for POS tagging, computational semantics, question answering, text summarization and dialogue systems.

High-Performance Computing (HPC) aspects will demonstrate how NLP can be leveraged on graphical processing units (GPUs) using Google TensorFlow and NLTK library. Focus is primarily upon the application of NLP to real-world problems, with some introduction to transformers and large language models, like ChatGPT, with practical exercises using.

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 theoretical and practical skills of technology of Big Data – massive amounts of information that necessitate software systems and resources with significantly enhanced storage, communication and processing and analysis algorithms beyond the capabilities of traditional databases and OLAP. The module introduces the programming paradigm and mindset that are required in this emerging field.

Topics include statistical modelling and inference, populations and samples, probability distributions, exploratory data analysis, fitting a machine learning model, linear regression, k-Nearest Neighbours (k-NN), k-Means, Naive Bayes, dimensionality reduction, singular value decomposition, principal component analysis, artificial neural networks and deep learning models. Further discussions will include mining social-network graphs, clustering of graphs, direct discovery of communities in graphs, partitioning of graphs, neighbourhood properties in graphs, data visualization, ethical and legal issues.

An appreciation of programming paradigm, tools, techniques and algorithms supporting Big Data will provide necessary practical experience. Students will implement algorithms in Python with relevant libraries for big data gathering, storage, manipulation and analyses.