Which of the following is NOT a type of machine learning?

• A. Supervised learning
• B. Unsupervised learning
• C. Reinforcement learning
• D. Heuristic programming

Answer: (D)

Types of learning in AI are defined by how the system improves or adapts with data. Three mainstream approaches all rely on data and feedback to shape behavior: supervised learning uses labeled examples to map inputs to outputs, unsupervised learning discovers structure or patterns in unlabeled data, and reinforcement learning improves decisions through trial and error guided by rewards from interacting with an environment. Heuristic programming, in contrast, relies on manually crafted rules, scoring functions, or decision heuristics that do not change unless a programmer updates them. It doesn’t learn from data, so it isn’t considered a type of machine learning. That’s why heuristic programming is the correct choice for NOT being a type of machine learning; the other three are established learning paradigms because they adapt based on data and experience.