Applied Artificial Intelligence MSc

This module will provide you with an introduction to the core concepts and techniques of artificial intelligence, from search algorithms, knowledge representation, and machine learning, to the ethical considerations of AI in society. You’ll gain a foundational understanding of AI's history, its evolution, and its implications for various industries.  

This module explores AI-driven decision support systems, their role in diverse industries, and their capability to enhance human decision-making. You’ll gain insights into AI applications for optimal outcomes in various scenarios, emphasising real-world applications, challenges, and ethical considerations. The module also explains the role of Simulation in Decision Support Systems and discusses the ethical implications and bias associated with automating the decision-making process.  

In this module, you’ll begin with foundational concepts and progress to cutting-edge deep learning techniques covering perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention models. You’ll gain hands-on experience designing and training these networks for various applications, while also addressing challenges in explainability, interpretability, and ethics.  

This module bridges the gap between theory and practical applications of machine learning. You’ll familiarise yourself with core techniques, ranging from supervised to unsupervised learning, and apply them in hands-on tasks. Emphasis is placed on the entire machine learning pipeline, including data pre-processing, model selection, and ethical considerations.  

This module is the culmination of the course requiring you to demonstrate your learning by identifying critical challenges, conducting rigorous literature reviews, and designing innovative AI solutions. You’ll undertake an individual software development project on an approved topic under the guidance of a supervisor. Throughout, you’ll evaluate results, address ethical considerations, and communicate findings in a professional manner to an appropriate audience.  

This module equips you with the foundational programming and mathematical skills essential for AI study. Topics include object-oriented programming in Python, AI-relevant data structures, graph theory, network analysis, and vital mathematical domains like linear algebra and probability. You’ll become skilled at applying key AI libraries and translating mathematical theory into practical AI solutions.  

This module equips you with the foundational algorithms, tools, and techniques needed to design, implement, and assess computer vision applications across various domains.  

This module focuses on distributed data storage, processing frameworks, and the design and deployment of scalable cloud-native applications. You’ll be trained to tackle the challenges associated with managing large volumes of data in the cloud, which is crucial for practical modern Artificial Intelligence (AI) solutions.