Module Question 3

What is the key difference between supervised and unsupervised learning? Provide an example for each.
What is “overfitting” in a machine learning model, and why is it a problem?
Describe the three main components of reinforcement learning: agent, environment, and reward.
How does a training dataset differ from a testing dataset, and why is this separation important?

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Supervised learning is when AI learns from labeled data. For example, AI learns to recognize cats and dogs from labeled pictures. Unsupervised learning is when AI learns from data without labels. For example, AI groups customers based on their shopping habits.
Overfitting happens when AI memorizes training data too much and has difficulty recognizing new data. As a result, its predictions may be less accurate.
– Agent: The AI that makes decisions or takes actions.
– Environment: The place or situation where the AI operates.
– Reward: Feedback the AI receives based on its actions.
For example, an AI playing a game gets a reward when it completes a mission and learns from the results of its actions.
A training dataset is used to teach AI to recognize patterns, while a testing dataset is used to check how well the AI performs after training. They must be separated to make sure the AI can handle new data instead of just memorizing the training data.

Module Question 3_BD107_Farhan_Al-Fathir_2681495024