The course is structured according to the AiU Certified Tester in AI syllabus. This way you can relate the topics covered in the course to the syllabus.
- Introduction to Artificial Intelligence: introducing artificial intelligence (AI), Machine Learning (ML) and Deep Learning (DL).
- Overview of testing AI systems: off-line and online testing of AI applications, data preparation and pre-processing (outlier detection, dimension reduction), imputation and visualization
- Offline Testing of AI Systems: Data Preparation and Preprocessing
- Metrics for supervised (Accuracy, Precision, Recall/sensitivity, Specificity and F1-score) and unsupervised learning (Inertia and Rand score, Support, Confidence and Lift metrics) to find the best AI model
- Explainable AI: examination and evaluation of complex (DL models) models by varying input variables and observing variations in outcomes while constructing a simple interpretable model
- Risks and test strategy for AI systems
- AI in testing: application of AI in the test process itself, smart dashboards and test automation tools