Category

Computing & AI

Level

Level 4

Qualification Number

610/6113/X

Total Credits

10

Course Overview

The ATHE Level 4 Award in Artificial Intelligence (10 credits) is a single-unit Award which introduces the history and key concepts of Artificial Intelligence. The qualification discusses basic types of Artificial Intelligence algorithms and enables the learner to apply an algorithm on a sample dataset.

As a short, sharp introduction to the key topics, this is particularly suited to learners already with a background in
computing and maths looking to upskill their knowledge and skills in this specific area.

Grading
Graded with Pass, Merit and Distinction.
Advanced learner loans available in the UK – to check if funding is available see the latest Qualification Catalogue here.
For the progression routes visit our progression routes page.

Delivery Mode
This qualification can be delivered either in the classroom, via distance learning or blended.

Typical Age

The qualification is designed for learners who are typically aged 18+

Qualifications

For learners who have recently been in education or training the entry profile is likely to include one of the following:

A GCSE Advanced level profile with achievement in 2 or more subjects supported by 5 or more GCSEs at grades 4/C and above
Other related Level 3 subjects such as ATHE Level 3 Diplomas
an Access to Higher Education Certificate delivered by an approved further education Institute and validated by an Access Validating Agency
Other equivalent international qualifications
Language

For those whom English is not their first language we recommend the following standards of proficiency in English language skills or an approved equivalent for this qualification:

IELTs 5.5
Common European Framework of Reference (CEFR) B2
Cambridge English Advanced (CAE) 162 or above
Pearson Test of English (PTE) Academic 42-49

Details Unit Aims
Introduction to Artificial Intelligence
Credits: 10
Mandatory: Yes
  1. Understand intelligence and computer models
  2. Understand types of machine learning algorithms
  3. Carry out machine learning on a sample dataset
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