Mark all cases where supervised learning should be applied as opposed to unsupervised learning:

Question

BlackTom AI · Accepted Answer

Consider what supervised learning requires: labeled examples that map inputs to known outputs, so the model can learn to predict the output for new inputs.
Option 1: 'You are given a database of text samples and for each of them, you know which language it is written in. You want to predict the language of a new text.' Here, there are labels (the language for each sample), so a model can learn to map text features to language labels. This is a classic supervised learning task.
Option 2: 'You are given a database of photos of people that are smiling and a database of pictures of people that are not smiling. You would like to recognize whether a person on a new picture is smiling or not.' This setup provides labeled examples (smiling vs not smiling). A model can learn to classify new photos based on those labels, which is exactly supervised learning.
Option 3: 'You are given a database of speech samples. You would like to split them into clusters that contain samples from the same person.' Clustering into groups of same-person samples is an unsupervised learning task, since there are no labels indicating which sample belongs to which person, only the goal to group similar samples.
Option 4: 'A set of people gave you information about their interests and hobbies. To each of them, you would like to suggest a few people they might like to meet.' This description describes a recommendation task that typically lacks explicit labels in this phrasing. Without known ground-truth likes/dislikes to learn from, this aligns more with unsupervised or semi-supervised techniques (e.g., clustering or collaborative filtering) rather than a straightforward supervised mapping unless labeled preferences are provided.
In summary, options 1 and 2 clearly align with supervised learning because labeled outputs are specified for the given inputs. Options 3 and 4 involve discovering structure or making suggestions without labeled target outputs, which fit unsupervised (or related) approaches rather than standard supervised learning.

DD2380 HT24 (AIHT24_2) Q4: Machine learning

Mark all cases where supervised learning should be applied as opposed to unsupervised learning:

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