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ISTQB AI Testing(CT-AI) | AI Testing Masterclass for Testers
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Introduction to Course
1. Introduction to AI
1.1 Definition of AI and AI Effect
1.2 Narrow, General and Super AI
1.3 AI-Based and Conventional Systems
1.4 AI Technologies
1.5 AI Development Frameworks
1.6 Hardware for AI-Based Systems
1.7 AI as a Service (AIaaS)
1.7.1 Contracts for AI as a Service
1.7.2 AIaaS Examples
1.8 Pre-Trained Models
1.8.1 Introduction to Pre-Trained Models
1.8.2 Transfer Learning
1.8.3 Risks of using Pre-Trained Models and Transfer Learning
1.9 Standards, Regulations and AI
Module 1 - Resources to download
2. Quality Characteristics for AI-Based Systems
2.1 Flexibility and Adaptability
2.2 Autonomy
2.3 Evolution
2.4 Bias
2.5 Ethics
2.6 Side Effects and Reward Hacking
2.7 Transparency, Interpretability and Explainability
2.8 Safety and AI
Module 2 - Resources to download
3. Machine Learning (ML) – Overview
3.1 Forms of ML
3.1.1 Supervised Learning
3.1.2 Unsupervised Learning
3.1.3 Reinforcement Learning
3.2 ML Workflow
3.3 Selecting a Form of ML
3.4 Factors Involved in ML Algorithm Selection
3.5 Overfitting and Underfitting
3.5.1 Overfitting
3.5.2 Underfitting
Module 3 - Resources to download
4 ML - Data
4.1 Data Preparation as Part of the ML Workflow
4.1.1 Challenges in Data Preparation
4.2 Training, Validation and Test Datasets in the ML Workflow- Part 1
4.2 Training, Validation and Test Datasets in the ML Workflow- Part 2
4.3 Dataset Quality Issues
4.4 Data Quality and its Effect on the ML Model
4.5 Data Labelling for Supervised Learning
4.5.1 Approaches to Data Labelling
4.5.2 Mislabeled Data in Datasets
Module 4 - Resources to download
5 ML Functional Performance Metrics
5.1 Confusion Matrix
5.2 Additional ML Functional Performance Metrics for Classification, Regression and Clustering
5.3 Limitations of ML Functional Performance Metrics
5.4 Selecting ML Functional Performance Metrics
5.5 Benchmark Suites for ML
Module 5 - Resources to download
6. ML - Neural Networks and Testing
6.1 Neural Networks
6.2 Coverage Measures for Neural Networks
Module 6 - Resources to download
7. Testing AI-Based Systems Overview
7.1 Specification of AI-Based Systems
7.2 Test Levels for AI-Based Systems
7.2.1 Input Data Testing
7.2.2 ML Model Testing
7.2.3 Component Testing
7.2.4 Component Integration Testing
7.2.5 System Testing
7.2.6 Acceptance Testing
7.3 Test Data for Testing AI-based Systems
7.4 Testing for Automation Bias in AI-Based Systems
7.5 Documenting an AI Component
7.6 Testing for Concept Drift
7.7 Selecting a Test Approach for an ML System
Module 7 - Resources to download
8. Testing AI-Specific Quality Characteristics
8.1 Challenges Testing Self-Learning Systems
8.2 Testing Autonomous AI-Based Systems
8.3 Testing for Algorithmic, Sample and Inappropriate Bias
8.4 Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
8.5 Challenges Testing Complex AI-Based Systems
8.6 Testing the Transparency, Interpretability and Explainability of AI Based Systems
8.7 Test Oracles for AI-Based Systems
8.8 Test Objectives and Acceptance Criteria
Module 8 - Resources to download
9. Methods and Techniques for the Testing of AI-Based Systems
9.1 Adversarial Attacks and Data Poisoning
9.1.1 Adversarial Attacks
9.1.2 Data Poisoning
9.2 Pairwise Testing
9.3 Back-to-Back Testing
9.4 A/B Testing
9.5 Metamorphic Testing (MT)
9.6 Experience-Based Testing of AI-Based Systems
9.7 Selecting Test Techniques for AI-Based Systems
Module 9 - Resources to download
10. Test Environments for AI-Based Systems
10.1 Test Environments for AI-Based Systems
10.2 Virtual Test Environments for Testing AI-Based Systems
Module 10 - Resources to download
11. Using AI for Testing
11.1 AI Technologies for Testing
11.2 Using AI to Analyze Reported Defects
11.3 Using AI for Test Case Generation
11.4 Using AI for the Optimization of Regression Test Suites
11.5 Using AI for Defect Prediction
11.6 Using AI for Testing User Interfaces
11.6.1 Using AI to Test Through the Graphical User Interface (GUI)
11.6.2 Using AI to Test the GUI
Module 11 - Resources to download
Conclusion
Introduction
Introduction to Course
Preview
1. Introduction to AI
1. Introduction to AI
Preview
1.1 Definition of AI and AI Effect
1.2 Narrow, General and Super AI
1.3 AI-Based and Conventional Systems
1.4 AI Technologies
1.5 AI Development Frameworks
1.6 Hardware for AI-Based Systems
1.7 AI as a Service (AIaaS)
1.7.1 Contracts for AI as a Service
1.7.2 AIaaS Examples
1.8 Pre-Trained Models
1.8.1 Introduction to Pre-Trained Models
1.8.2 Transfer Learning
1.8.3 Risks of using Pre-Trained Models and Transfer Learning
1.9 Standards, Regulations and AI
Module 1 - Resources to download
2. Quality Characteristics for AI-Based Systems
2. Quality Characteristics for AI-Based Systems
Preview
2.1 Flexibility and Adaptability
Preview
2.2 Autonomy
2.3 Evolution
2.4 Bias
2.5 Ethics
2.6 Side Effects and Reward Hacking
2.7 Transparency, Interpretability and Explainability
2.8 Safety and AI
Module 2 - Resources to download
3. Machine Learning (ML) – Overview
3. Machine Learning (ML) – Overview
Preview
3.1 Forms of ML
3.1.1 Supervised Learning
3.1.2 Unsupervised Learning
3.1.3 Reinforcement Learning
3.2 ML Workflow
3.3 Selecting a Form of ML
3.4 Factors Involved in ML Algorithm Selection
3.5 Overfitting and Underfitting
3.5.1 Overfitting
3.5.2 Underfitting
Module 3 - Resources to download
4. ML - Data
4 ML - Data
Preview
4.1 Data Preparation as Part of the ML Workflow
4.1.1 Challenges in Data Preparation
4.2 Training, Validation and Test Datasets in the ML Workflow- Part 1
4.2 Training, Validation and Test Datasets in the ML Workflow- Part 2
4.3 Dataset Quality Issues
4.4 Data Quality and its Effect on the ML Model
4.5 Data Labelling for Supervised Learning
4.5.1 Approaches to Data Labelling
4.5.2 Mislabeled Data in Datasets
Module 4 - Resources to download
5. ML Functional Performance Metrics
5 ML Functional Performance Metrics
Preview
5.1 Confusion Matrix
5.2 Additional ML Functional Performance Metrics for Classification, Regression and Clustering
5.3 Limitations of ML Functional Performance Metrics
5.4 Selecting ML Functional Performance Metrics
5.5 Benchmark Suites for ML
Module 5 - Resources to download
6. ML - Neural Networks and Testing
6. ML - Neural Networks and Testing
Preview
6.1 Neural Networks
6.2 Coverage Measures for Neural Networks
Module 6 - Resources to download
7. Testing AI-Based Systems Overview
7. Testing AI-Based Systems Overview
Preview
7.1 Specification of AI-Based Systems
7.2 Test Levels for AI-Based Systems
7.2.1 Input Data Testing
7.2.2 ML Model Testing
7.2.3 Component Testing
7.2.4 Component Integration Testing
7.2.5 System Testing
7.2.6 Acceptance Testing
7.3 Test Data for Testing AI-based Systems
7.4 Testing for Automation Bias in AI-Based Systems
7.5 Documenting an AI Component
7.6 Testing for Concept Drift
7.7 Selecting a Test Approach for an ML System
Module 7 - Resources to download
8. Testing AI-Specific Quality Characteristics
8. Testing AI-Specific Quality Characteristics
Preview
8.1 Challenges Testing Self-Learning Systems
8.2 Testing Autonomous AI-Based Systems
8.3 Testing for Algorithmic, Sample and Inappropriate Bias
8.4 Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems
8.5 Challenges Testing Complex AI-Based Systems
8.6 Testing the Transparency, Interpretability and Explainability of AI Based Systems
8.7 Test Oracles for AI-Based Systems
8.8 Test Objectives and Acceptance Criteria
Module 8 - Resources to download
9. Methods and Techniques for the Testing of AI-Based Systems
9. Methods and Techniques for the Testing of AI-Based Systems
Preview
9.1 Adversarial Attacks and Data Poisoning
9.1.1 Adversarial Attacks
9.1.2 Data Poisoning
9.2 Pairwise Testing
9.3 Back-to-Back Testing
9.4 A/B Testing
9.5 Metamorphic Testing (MT)
9.6 Experience-Based Testing of AI-Based Systems
9.7 Selecting Test Techniques for AI-Based Systems
Module 9 - Resources to download
10. Test Environments for AI-Based Systems
10. Test Environments for AI-Based Systems
Preview
10.1 Test Environments for AI-Based Systems
10.2 Virtual Test Environments for Testing AI-Based Systems
Module 10 - Resources to download
11. Using AI for Testing
11. Using AI for Testing
Preview
11.1 AI Technologies for Testing
11.2 Using AI to Analyze Reported Defects
11.3 Using AI for Test Case Generation
11.4 Using AI for the Optimization of Regression Test Suites
11.5 Using AI for Defect Prediction
11.6 Using AI for Testing User Interfaces
11.6.1 Using AI to Test Through the Graphical User Interface (GUI)
11.6.2 Using AI to Test the GUI
Module 11 - Resources to download
Conclusion
Conclusion
Preview
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