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4 cards from this section. Quick, good the night before a test.
What is modelling in the AI Project Cycle?
Think about the learning process of AI.
Name one algorithm used for classification tasks.
Consider algorithms that categorize information.
What is the purpose of modelling in AI?
Think about what happens after data has been collected.
True or False: Testing is not necessary if the training data is large enough.
Consider the importance of evaluating even well-trained models.
Design a detailed experimental setup for training an AI model to predict credit scores based on user financial data. Include the types of algorithms you would consider and why.
Consider what data features would contribute significantly to predicting an outcome.
Critically analyze a scenario where deploying a poorly trained AI model could have real-world consequences. Discuss how to circumvent that situation during the modelling phase.
Think about the ethical implications and necessary steps during the training and testing phases.
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