What best describes the difference between traditional programming and machine learning?

What best describes the difference between traditional programming and machine learning?

• A. Traditional programming relies on explicitly coding rules and logic, while machine learning learns from data and improves without explicit programming.
• B. Traditional programming learns from data; machine learning relies on explicit rules.
• C. Traditional programming uses probabilistic models; machine learning uses deterministic code.
• D. Machine learning cannot improve over time; traditional programming can.

Answer: (A)

The key distinction is how the system produces behavior: traditional programming encodes knowledge as explicit rules and logic, while machine learning builds a model by learning from data. In traditional programming, you write the exact steps the computer should take for each input, so the output is determined by the coded rules and doesn’t improve unless you rewrite the code. In machine learning, you don’t specify every rule; you provide data and a learning algorithm, and the model adjusts its internal parameters to capture patterns. This lets the model generalize to new data and improve as you feed more data or retrain it. The other statements get this relationship backwards or mischaracterize the nature of improvement, whereas the best description emphasizes data-driven learning versus hand-crafted rules.

The key distinction is how the system produces behavior: traditional programming encodes knowledge as explicit rules and logic, while machine learning builds a model by learning from data. In traditional programming, you write the exact steps the computer should take for each input, so the output is determined by the coded rules and doesn’t improve unless you rewrite the code. In machine learning, you don’t specify every rule; you provide data and a learning algorithm, and the model adjusts its internal parameters to capture patterns. This lets the model generalize to new data and improve as you feed more data or retrain it. The other statements get this relationship backwards or mischaracterize the nature of improvement, whereas the best description emphasizes data-driven learning versus hand-crafted rules.