Instance-Based Learning is an AI approach where models make predictions based on similarity to previously seen examples. Instead of generalising from a predefined set of rules, Instance-Based Learning examines specific instances and their characteristics. This approach is effective in tasks such as pattern recognition and recommendation systems.
Adversarial machine learning involves studying and defending AI models against attacks or adversarial examples designed to deceive the system. By understanding vulnerabilities and deploying…