MSc in Artificial Intelligence

Study modules

Compulsory modules

Introduction to Machine Learning (10 credits)

This module studies the fundamentals of machine learning methodologies, implementations and analysis methods appropriate for machine learning applications. Compulsory for MSc and PG Dip.

Foundations of Artificial Intelligence (AI) (10 credits)

This module teaches the fundamental concepts of AI including classical and modern approaches to AI and the philosophical bases of AI. Compulsory for MSc and PG Dip.

Autonomous Intelligent Systems (20 credits)

This module equips you with the knowledge and critical understanding of how Autonomous Intelligent Systems are employed in a wide range of environments with different functionalities. Compulsory for MSc.

AI Systems Engineering (20 credits)

This module provides students with skills to critically evaluate and analyse the application of artificial intelligence in the domain of systems engineering. Compulsory for MSc.

Data Visualisation (10 credits)

This module covers state of the art tools and techniques to build useful visualisations for different types of data sets and application scenarios. Compulsory for MSc.

Big Data and the Cloud (20 credits)

The cloud has become a key part of modern life and with it comes vast amounts of data. This module looks at how clouds work and can be used to tackle the big data challenges of modern science and business. Compulsory for MSc.

Research, Scholarship and Professional Skills (20 credits)

Advanced Machine Learning (10 credits)

This module equips students with skills to critically evaluate complex machine learning algorithms in different application scenarios. Compulsory for MSc.

Dissertation in Computing Subjects (60 credits)

This is an individual research and development project that allows you to study a topic of your choice in the area of Artificial Intelligence in-depth, guided by your supervisor. The work may be undertaken in close cooperation with a research, industrial or commercial organisation. You undertake your dissertation over the summer period if you are a full-time student.

independent Study II (20 credits)

Learning and teaching

You will be taught with a combination of lectures and practical sessions. Lectures provide a theoretical basis, while practical sessions strengthen your understanding with active involvement.

You will be provided with a varied experience, as well as the opportunity to discuss your work directly with lecturers.

Many of the modules are enriched by the teaching staff's research expertise. We also invite visiting lecturers from research organisations and industries to come and give guest lectures.

We focus on using industry-standard tools to solve practical and industrially relevant problems, and using those problems to teach theoretical concepts. This ensures that students have the opportunity to acquire skills that will not just equip them for today's computing industry, but for a lifelong career in the computing industry.

Careers

Graduates, from the programme, will be ideally equipped for a career in a wide variety of industries. Graduates are employed across a whole range of jobs, including

• data scientist
• software data engineer
• machine-learning engineer
• machine-learning scientist
• AI architect
• AI consultant
• AI specialist
• ML architect
• knowledge engineer.