Learning Objectives for Key Aspects of Artificial Intelligence
- What are Human Intelligence and Artificial Intelligence?
- History of AI
- Symbolic AI
- Sub-symbolic AI
- Some ML Algorithms in More Detail
- Applications and Limits of AI
What are Human Intelligence and Artificial Intelligence?
- Types of Intelligence
- Turing Test
History of AI
- Main Periods of AI History
- Difference between Symbolic and Sub-symbolic AI
Symbolic AI
- Mathematical Logic and Inference
- Knowledge-based Systems
- Constraint-based Solving Systems for Problem Solving
Sub-symbolic AI
- Types of Learning
- Examples of Applications of Different Types of Learning
- Machine Learning Algorithms
- Machine Learning Metrics
Some ML Algorithms in More Detail
- Bayesian Belief Networks
- Naïve Bayes classifier
- Support Vector Machine Algorithm
- K-means Algorithm
- Artificial Neural Networks: Perceptron Learning Algorithm
Applications and Limits of AI
- Activities of Machine Learning
- Possible Biases in AI Systems
- Ethical Issues in AI Systems
2.0 Testing Artificial Intelligence Systems
- General Problems with Testing AI Systems
- Machine Learning Model Training and Testing
- AI Test Environments
- Strategies to Test AI-based Systems
- Metrics for Testing AI-based Systems
General Problems with Testing AI Systems
- Software Which Is Written To Calculate an Output for Which the Correct Answer Is Not Known
- Real-world Inputs
- Self-optimization
- Expert Systems
- Perception of Intelligence
- Model Optimization
- AI System Quality Characteristics
- Bias/Variance Trade Off and the No Free Lunch Theorem
- Drift
- Ethical Considerations
- Automation Bias
- Adversarial Actors
Machine Learning Model Training and Testing
- AI Test Environments
- Strategies to Test AI-based Systems
- Acceptance Criteria
- Functional Testing
- Levels of Testing
Metrics for Testing AI-based systems
- Confusion Matrix
- Statistical Significance
Using AI to Support Testing
- AI in Testing
- Applying AI to Testing Tasks and Quality Management
- AI in Component Level Test Automation
- AI in Integration Level or System Level Test Automation
- AI-based Tool Support for Testing
AI in Testing
- The Oracle Problem
- Test Oracles
- Testing versus Test Automation
Applying AI to Testing Tasks and Quality Management
- Tasks AI Can Be Applied To
- Tasks AI Cannot Be Applied To
- Using AI for Test Data Generation
- Using AI for Bug Triaging
- Using AI for Risk Prediction and Fault Estimation
AI in Component Level Test Automation
- AI in Component Level Test Generation
- AI in System Level Test Generation
AI in Integration Level or System Level Test Automation
- Monkey Testing Versus Fuzz Testing
- AI For Test Generation on the System Level
- AI For Test Selection and Prioritization
- AI For Object Identification and Identifier Selection
- AI For Visual Test Automation
AI-based Tool Support for Testing
- Relevant Metrics in an AI-Based Testing Approach
- Assess Tool Vendor Claims
- Configuration of the System
- Return on Investment (ROI)
- Effects on Existing Processes
- Sensibility of Test Cases
- Test Case Explosion
- Maintainability
- Severity of the Defects Found
