Machine Learning Scenario Quiz

What we verified before publishing

Fact-check status: passed

The following claims were verified through Perplexity Sonar before the questions were finalised. The host can reference any of them during play:

• Machine learning is a subfield of artificial intelligence focused on algorithms that learn patterns from data to make predictions without explicit instructions[1][3][7].
• Arthur Samuel defined machine learning in 1959 as the field of study that gives computers the ability to learn without being explicitly programmed[1][8].
• The three main types of machine learning are supervised learning, unsupervised learning, and reinforcement learning[2][4][8].
• Supervised learning trains models on labeled data to predict or classify new, unseen data[4].
• Unsupervised learning finds patterns or groups in unlabeled data, such as clustering or dimensionality reduction[4].
• Reinforcement learning trains machines through trial and error to take the best action by establishing a reward system[1][4].
• Machine learning systems can be descriptive (explain what happened), predictive (predict what will happen), or prescriptive (suggest actions to take)[1].
• The fundamental goal of machine learning is generalization, translating strong performance on training data to useful results in real-world scenarios[3].
• Raw data must be cleaned and transformed for training, including handling missing values, normalizing data, and encoding text into numeric formats[5][8].
• Machine learning models learn patterns by identifying mathematical correlations between input and expected output from training data[3][5].
• Machine learning uses data like bank transactions, photos of people, repair records, sensor time series, and sales reports as training input[1].
• Deployment of an AI model is called AI inference, where the model applies learned patterns to infer the correct output for a real-world task[3].