ATHE Level 5 Certificate in Artificial Intelligence
Introduction
This two-unit, 30-credit qualification builds on the Level 4 Award and extends specific Artificial Intelligence knowledge and skills by requiring learners to get to grips with underpinning advanced statistics and natural language processing. This qualification would be particularly suited to learners with a background in maths and computing looking to upskill their knowledge and skills in this specific area.
Grading
Graded with Pass, Merit and Distinction.
ATHE Level 5 Certificate in Artificial Intelligence (30 credits)
Pass 90 – 107
Merit 108 – 134
Distinction 135
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.
Qualification Specification
To view the specification, please click here.
Additional
This qualification is eligible for UCAS points. To find out how much points your qualification is worth, please visit the UCAS Tariff Calculator here.
Typical Age
These qualifications are designed for learners who are typically aged 18+.
Qualifications
The entry profile for learners is likely to include at least one of the following:
- Prior study in computing or related subjects at Level 4 or above, or a Level 4 qualification, for example, an ATHE Level 4 Diploma/Extended Diploma in Computing; a Higher Technical Qualification (HTQ) in a computing-related subject
- 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
Learners are required to achieve both mandatory units.
| Unit Name | Unit Aims | Credits | Mandatory |
|---|---|---|---|
| Advanced Statistics for Data Science | This unit introduces statistical and probability concepts for understanding statistical models. It enables learners to numerically evaluate the performance of algorithmic models and equips them with practical knowledge of statistical modelling techniques such as regression, Bayes classifiers and support vector machines. The learners will undertake a practical activity in applying one of these algorithms to data | 15 | Yes |
| Foundations of Artificial Intelligence for Data and Language | This unit introduces the learners to the processes of how machines learn from data. It covers the basics of natural language processing as well as applying these techniques to a dataset. Learners will also investigate how generative AI models work, their strengths and weaknesses, and how such models can be used as components in computer systems | 15 | Yes |