Last modified: 22 May 2019 17:07
How do we assess whether an AI system works and is effective? Indeed, what does it mean for an AI system to be effective? In this course, we will look at different ways of evaluating AI systems, including performance on benchmark data sets, usefulness at helping users achieve a task, and subjective opinions (ie, do people like the system). Much of the course is devoted to statistics (including the R programming language), experimental design, and ethical issues. In practical and assessment work, students will evaluate deployed AI systems, and also critique evaluations in published AI research papers.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Old Aberdeen | Sustained Study | No |
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The course will cover concepts, methods, techniques and tools/technologies for evaluating AI systems. Students will be equipped with knowledge on statistical analysis (e.g., variance, correlations and regression) and learn to use software/tools for statistical analysis. The course will introduce criteria for the evaluation of AI systems (e.g., usability, accessibility and learnability), and the theoretical evaluation of AI systems (e.g., guarantees regarding correctness, completeness, complexity, admissibility of heuristics, and so on). The course will provide a comprehensive exposition to issues pertaining to the empirical evaluation of AI Systems, including the design of experiments (to address specific criteria/issues), human-driven experiments (including the design of forms and questionnaires, interviews, “talk-aloud” experiments, logging/filming, etc.), systems with optimal behaviours vs. (sub-optimal) human-like behaviour, crowd-sourcing of experiments (including Amazon’s “Mechanic Turk” and others), evaluation through gaming, and other related topics.
Information on contact teaching time is available from the course guide.
Group report (50%); Individual report (50%).
Resit: where a student fails the course overall they will be afforded the opportunity to resit those parts of the course that they failed (pass marks will be carried forward).
There are no assessments for this course.
Formative feedback for in-course assessments will be provided in written form. Additionally, formative feedback on performance will be provided informally during practical sessions.
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