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  • DeadlineStudy Details: 1-2 Years

Masters Degree Description

In a world reliant on data to improve processes and monitor success our Masters degree in Artificial Intelligence will help you gain the knowledge and skills to improve systems in your industry – or create better ones. It's been designed in response to the high demand for experts in data analysis and modelling.

Study at ARU in Cambridge and build on your existing skills. Our course is suitable for computer scientists software engineers IT professionals or established professionals in the areas of computing mathematics and/or data analysis. Before starting the course we strongly recommended that you have basic programming knowledge of Python.

As well as our 12 and 20 month full-time courses there is also the option to study with us for two years and gain valuable experience in Year 2 with a choice of professional experience modules.

Entry Requirements

A first degree with honours at a minimum final classification of Lower Second (2.2) or equivalent in a Computing Electronics Mathematics or Statistics subject.
If English is not your first language you will be expected to demonstrate a certificated level of proficiency of at least IELTS 6.5 ( Academic level) or equivalent English Language qualification as recognised by Anglia Ruskin University.

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Fees

For fees and funding options, please visit website to find out more

Student Destinations

Companies are looking for graduates with skills in machine learning and artificial intelligence techniques that our Masters degree provides.

After graduating you’ll be well prepared for both employment and academic research. You may wish to apply your skills in job roles where there is a need for AI-enabled business strategies and marketing management of database records e-business technologies IT system development and design or IT project management.

Module Details

Core modules
Semantic Data Technologies (30 credits)
Advanced Machine Learning (30 credits)
Research Methods (15 credits)
Applications of Machine Learning (15 credits)
Neural Computing and Deep Learning (30 credits)
Postgraduate Major Project (60 credits)
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