Last modified: 05 Dec 2025 16:46
One of the biggest challenges in Artificial Intelligence is evaluating how well AI systems work. This course will provide students of our MSc in AI with knowledge of core evaluation concepts, approaches, tools, techniques and technologies; we will also look at software testing of AI systems.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Offshore | Sustained Study | No |
| Co-ordinators |
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The course will cover concepts, methods, techniques and tools/technologies for evaluating and testing AI systems. Students will be equipped with knowledge on statistical analysis and hypothesis testing (e.g., t-test, chi-square, correlations and regression) and learn to use software/tools for statistical analysis. The course will cover the design and execution of human evaluations of AI systems, including research ethics when working with human subjects. It will also examine the use of automated metrics to evaluate AI systems, including best practice, common pitfalls, and avoiding bias; and methodologies for rigorous software testing of AI systems.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 50 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Reflection | Analyse | Students will be familiar with common AI evaluation techniques, and can critically assess whether these techniques are being used appropriately. |
| Reflection | Create | Students will be able to design, carry out, and write up experiments to test research questions and hypotheses |
| Reflection | Create | Students will understand how to perform common statistical hypothesis tests. |
| Assessment Type | Summative | Weighting | 50 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Reflection | Analyse | Students will be familiar with common AI evaluation techniques, and can critically assess whether these techniques are being used appropriately. |
| Reflection | Create | Students will understand how to perform common statistical hypothesis tests. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | ||
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback | ||||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Reflection | Analyse | Students will be familiar with common AI evaluation techniques, and can critically assess whether these techniques are being used appropriately. |
| Reflection | Create | Students will understand how to perform common statistical hypothesis tests. |
| Reflection | Create | Students will be able to design, carry out, and write up experiments to test research questions and hypotheses |
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