Course · AI6302
Theory of Experimental Validation in AI & Systems
Course information
- Course code
- AI6302
- Credits (ECTS)
- 4
- L + S + Lab
- 0 + 0 + 0
- Year / semester
- Year 1 / semester 1
- Level of course
- Doctoral degree (PhD)
- Component
- Elective component (EC)
- Cycle
- PD
- Final assessment
- Examination
- Educational programme
- 8D06102 Computer and software engineering
- Curriculum
- Компьютерная и программная инженерия (2026-2029)
Elective course; offered as a choice together with:
Course description
Upon completion of this course, students will be able to design valid experiments for evaluating AI models, select and justify quality metrics, apply statistical methods for verifying the significance of results, evaluate the robustness, generalization ability, and overfitting of models, analyze sources of bias and experimental errors, ensure the reproducibility of experiments, correctly format the Experimental Validation section, and interpret the results in terms of practical and business value.
Source: Platonus, working curriculum for the 2026–2027 academic year.
← Programme curriculum