More than 20 billion microprocessors and microcontrollers are currently providing intelligent features, smart capabilities, personalised interfaces, optimised communications to an incredibly wide range of devices. From automotive to healthcare, from science and industry to social science and finance, embedded computing is at the very hearth of almost all modern digital systems. Explore the world of Embedded Computing and learn to employ Machine Learning methodologies to create market-ready solutions with enhanced technologies to transform data into valuable information. Learn to combine hardware and software components, making the most of powerful portable computing and advanced data-driven decision-making. Our brand new degree course has been designed with employers and industry in mind, ensuring you are learning the professional skills you will need once you graduate. You will be guided through the cutting-edge tools to learn trends and behaviours from the data itself. You will be provided with the knowledge and skills to develop as a specialist in the areas of data analysis, machine learning, neural networks and artificial intelligence algorithm design. You will also gain the knowledge needed to display good professional judgement and responsibility in addressing commercial, environmental and ethical factors found in the various areas where Machine Learning and Automation are used. If you enjoy computer science, mathematics and technology and want to apply these fundamentals in a practical way – combining them with electronics, digital systems and software design to create practical solutions to everyday technological issues, this BSc top-up year is the right course for you.

Core Modules Year 1: Final Project. Artificial Intelligence.Digital Signal Processing.Digital Systems.Deep Learning.Cloud Computing.Embedded Computing

We'll assess you in a number of ways including time-constrained assessments, coursework assignments and a project. Our dissertation project and module case studies assess your ability to analyse situations, identify key issues, select, synthesise and apply techniques and skills from different modules, and evaluate the appropriateness of solutions when compared to industrial practice. The dissertation artefact will be based on a real-world scenario related to, or part of, a piece of project work in a company or relevant to industry.

Professional Bodies are not listed for this Course.

26 January This is the deadline for applications to be completed and sent for this course. If the university or college still has places available you can apply after this date, but your application is not guaranteed to be considered.

Course code: I403

Institution code: A60

Campus Name: Cambridge Campus

Campus code:

The following entry points are available for this course:

Year 1

Entry Requirements for Advanced Entry are not listed for this Course.

Entrants will normally be required to hold 120 Level 4 & 120 Level 5 credits ( i.e HND) from a relevant subject, or equivalent. Alternative qualifications may be accepted.

Please click the following link to find out more about qualification requirements for this course

https://aru.ac.uk/study/undergraduate/embedded-computing-and-machine-learning

Minimum Further Information are not listed for this Course.

Test	Grade	AdditionalDetails

English Language Entry Requirement Information are not listed for this Course.

Student satisfaction : 68%

Employment after 15 months (Most common jobs): 50%

Go onto work and study: 65%

England	9250.0	Year 1
Northern Ireland	9250.0	Year 1
Scotland	9250.0	Year 1
Wales	9250.0	Year 1
Channel Islands	9250.0	Year 1
Republic of Ireland	9250.0	Year 1

https://aru.ac.uk/student-life/preparing-for-university/help-with-finances/undergraduate 2022 International fees are to be confirmed

East Road
Address2 are not listed for this Course.
Address3 are not listed for this Course.
Cambridge
CB1 1PT