AiU® Certified Tester in AI
The course is providing a good introduction and overview of artificial intelligence methods used nowadays, starting from basic definitions to the different forms of AI model testing, online as well as offline. The particularities of risks, quality attributes and strategies for testing AI applications are outlined. In the last part it is demonstrated how AI is making testing tools smarter. Following this course will lead to a broad understanding of the topic.
After the course, successful participants will be able to:
· Understand current trends, industry applications of Artificial Intelligence (AI) using Machine Learning (ML).
· Compare different implemented ML algorithms to help choose the most suitable one.
· Evaluate models for both supervised and unsupervised learning.
· Design and execute test cases for AI systems.
· Use various methods for bringing transparency into model workings.
· Define a test strategy for testing of AI systems.
· Understand where AI can be used in manual testing and in test automation.
· Use AI based test execution tools to automate tests.
On top of that, if you pass the exam, you will hold the “AiU Certified Tester in AI” certificate.
· Testing professionals wishing to widen their testing scope towards testing of Artificial intelligence applications.
· Testing professionals wishing to acquire more in-depth knowledge of Artificial intelligence in test tools.
· Other stakeholders who wish to have a deeper understanding of Artificial intelligence in general and testing in particular.
· Everyone preparing for the “AiU Certified Tester in AI” examination.
To follow the course, trainees are recommended to:
· Hold the ISTQB® Certified Tester - Foundation Level certificate (CTFL) or equivalent.
· Have basic knowledge of any programming language – Java/Python/R
· Have basic knowledge of statistics
· Have some software development or testing experience
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
· 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
· 40 multiple-choice questions.
· No negative marking.
· Pass mark of 65%.
· Duration of 60 minutes.
WE FORSEE FOR you
· Printed course hand-outs.
· A copy of the AiU syllabus.
· Practical assignments, along with their solutions.
· Sample exam questions and answers.