Artificial Intelligence - MSc (Online) Programme content

Certificate in Artificial Intelligence 

Core modules

• Introduction to Scientific Computing for AI (M) (CE4021) Introduces 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.  

• Introduction to Deep Learning and Frameworks (CE4031) Previews 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. 

Exit option here 

Books and journal articles needed for the course will be available online through the UL Glucksman Library.  

For more information on each module, you can search the faculty, school and module code on UL’s Book of Modules 

Master’s programme start 

Core modules

• Artificial Intelligence and Machine Learning (M) (CE6002) Introduces the core concepts in machine learning and familiarising you with the theory that underpins statistical machine learning. Provides important insights into why and when machine learning is possible, and how to ensure the best performance possible.   

• Data Analytics (M) (CS5062) Introduces 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.  

Books and journal articles needed for the course will be available online through the UL Glucksman Library.  

For more information on each module, you can search the faculty, school and module code on UL’s Book of Modules 

Core modules

• Advanced Topics Seminars and Project Specification (CS6163) Introduces 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.  Note: This module 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. 

• Risk, Ethics, Governance and Artificial Intelligence (IN5103) 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.   

Books and journal articles needed for the course will be available online through the UL Glucksman Library.  

For more information on each module, you can search the faculty, school and module code on UL’s Book of Modules 

Modern Machine Learning Stream

Core modules

• Machine Learning Applications (ET5003) Introduces you to advanced machine learning models and applications, including Natural Language Processing and probabilistic approaches to machine learning. 

• Machine Vision (CE6003) 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. 

Natural Language Processing Stream

Core modules

• Natural Language Processing: An Introduction (MN5001) Introduces the world of Natural Language Processing (NLP), this module covers the fundamentals of statistical NLP, and its techniques and applications with a foundational approach 

• Information Retrieval (MN5151) 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. 

Computer Vision Stream

Core modules

• Deep Learning for Computer Vision (CE5021) Discusses the key computer vision tasks of image classification, object detection, semantic segmentation and facial recognition 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. 

• Machine Vision and Image Processing (CE5011) Introduces 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. 

Books and journal articles needed for the course will be available online through the UL Glucksman Library.  

For more information on each module, you can search the faculty, school and module code on UL’s Book of Modules 

Modern Machine Learning Stream

Core modules

• Deep Learning (CS5004) Takes 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. 

• Artificial Intelligence and Data Science Ecosystems: Theory and Practice (CS6512) 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. 

Natural Language Processing Stream

Core modules

• Advanced Natural Language Processing (MN5002) 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.  

• Natural Language Understanding (MN5162) 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.  

Computer Vision Stream

Core modules

• Geometric Computer Vision (CE5002) 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. 

• Intelligent Visual Computing & Applications (CE5012) 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.  

Books and journal articles needed for the course will be available online through the UL Glucksman Library.  

For more information on each module, you can search the faculty, school and module code on UL’s Book of Modules 

All streams

Core module

• Research/Development Project (ED5005) 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. 

Books and journal articles needed for the course will be available online through the UL Glucksman Library.  

For more information on each module, you can search the faculty, school and module code on UL’s Book of Modules