Artificial Intelligence - MSc (Online)

Autumn (Certificate &MSc)

CE4021 - Introduction To Scientific Computing For AI (M)

An introduction to the core mathematics and core programming skills required in machine learning. Using a number of E-tivities you will hone your Python coding skills as well as your knowledge and skills in Calculus, Linear Algebra and Probability Theory as the three core areas of mathematics that underpin machine learning.

CE4031 - Introduction To Deep Learning And Frameworks   

A sneak preview of the exciting possibilities that modern machine learning offers, introducing you to the core methods used in machine learning and state-of-the-art networks, such as Convolutional Neural Networks.

Spring (MSc)

CE6002 - Artificial Intelligence And Machine Learning (M)

An  introduction to the core concepts in machine learning and familiarising you with the theory that underpins statistical machine learning. This module provides important insights into why and when machine learning is possible, and how to ensure the best performance possible.

CS5062 - Data Analytics (M)

An introduction to a large number of practical skills used in machine learning,  including approaches to pre-processing data, using this data to train various machine learning algorithms, and methods to visualise the data and the performance of your machine learning models.

Note: Summer module Advanced Seminars and Project Specification begins in Week 1 of the Spring semester with a number of workshops and seminars, but all graded elements are due in the summer semester. 

Summer  (May -Jun)

CS6163 - Advanced Topics Seminars and Project Specification 

You will be introduced to a number of advanced topics through seminars (commencing in the Spring semester) to help you decide on your topic of interest for your project. You will also learn about the crucial research methods required to successfully conduct a Master’s level research project and write a literature review on the topic of your choice.

IN5103 - Risk, Ethics, Governance And Artificial Intelligence  

A crucial element of your education as a responsible AI engineer, this module will introduce you to the risks and ethical issues associated with Artificial Intelligence.

ET5003 - Machine Learning Applications 

This module will introduce you to advanced machine learning models and applications, including Natural Language Processing and probabilistic approaches to machine learning.

CE6003 - Machine Vision 

Covers traditional methods of machine vision, as well as act as an introduction to the exciting area of deep learning, which has driven many of the most recent innovations in machine vision.

MN5001 - Natural Language Processing: An Introduction 

An introduction to the world of Natural Language Processing (NLP), this module covers the fundamentals of statistical NLP, and its techniques and applications with a foundational approach

MN5151  - Information Retrieval

This module introduces students to the fields of Information Retrieval, Information Extraction, and Semantic Web. The module will cover a blend of fundamental concepts and current tools, techniques, and technologies used in modern information retrieval systems. 

CE5021 - Deep Learning for Computer Vision

In this module the application of deep learning to the key computer vision tasks of image classification, object detection, semantic segmentation and facial recognition is discussed in detail along with fundamental concepts in the design and structure of deep neural networks. Students gain a full understanding of how to design and build networks for their own applications.

CE5011 - Machine Vision and Image Processing

This module will introduce students to the principles of Machine Vision & Image Processing. Key topics such as linear image processing, feature detection and basic object detection are introduced with practical examples of these techniques.

CS5004 - Deep Learning 

Taking an in-depth look at deep learning theory and practice, you will learn about the most important deep neural network architectures as well as the most important deep learning frameworks.  You will then apply your newfound knowledge to a number of sample applications.

CS6512 - Artificial Intelligence and Data Science Ecosystems: Theory And Practice 

This module shows the two opposite sides of machine learning practice. On the one side, you will take a closer look at the algorithms that drive machine learning. On the other side, you will see how you can leverage these algorithms in AI workflows whilst minimising the coding effort through a model-driven design approach.  

MN5002 - Advanced Natural Language Processing

This module covers advanced level topics in natural language processing, with a focus on deep learning-based approaches. These include text classification, synthetic parsing, part of speech tagging, named-entity recognition, coreference resolution, and machine translation. 

MN5162 - Natural Language Understanding

This module introduces students to the field of Natural Language Understanding and related topics including sentiment analysis, relation extraction, natural language inference, semantic parsing, question answering, language generation, and conversational agents. 

CE5002 - Geometric Computer Vision

Geometric computer vision is the process of determining the structure of the environment, the position, orientation and movement of the camera with respect to the environment, through the analysis of camera image streams. Students will gain a practical understanding of its use in mobile robotics, vehicle autonomy and augmented reality.

CE5012 - Intelligent Visual Computing & Applications

This module focuses on applications of Deep-learning to important Computer Vision applications including Facial Recognition and 3D reconstruction. The use of transformer networks to build state-of-the art computer vision system is also discussed.

ED5005 - Research/Development Project 

The project which you have been working on throughout the summer of Year 1 and all of Year 2 is due in this semester. There are generally two options for submission: one at the start of the summer and one at the end of the summer, thus allowing you to finalise your project and dissertation during the summer of Year 2.