When comparing expert systems to machine learning, what is the major difference, and when is one preferred over the other? Explain your answer in 3-5 sentences.
The major difference between expert systems and machine learning is the way knowledge is acquired and represented. Expert systems use a set of predefined rules and knowledge bases created by human experts, while machine learning algorithms learn from data to generate their own rules and make predictions.
Expert systems are preferred in situations where the problem is well-defined and the rules are known, allowing the system to provide explainable and interpretable results. On the other hand, machine learning is preferred when the problem is complex and there is a large amount of data available, as machine learning algorithms can learn patterns and make predictions without the need for explicit rules or knowledge bases.