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Computer Science
Posted 3 months ago
Explain the differences and use-cases of supervised, unsupervised, and reinforcement learning in AI.
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Posted 3 months ago
Differences and Use-Cases of AI Learning Paradigms
Supervised Learning: This involves training a model on a labeled dataset, which means that each training example is paired with an output label
Use-Case for Supervised Learning: It is commonly used in applications where historical data predicts future events, such as in fraud detection, spam filtering, and risk assessment
Unsupervised Learning: This type of learning involves training a model on data that does not have labeled responses. The system tries to learn the patterns and the structure from the data without external guidance
Use-Case for Unsupervised Learning: It is used for clustering and association problems, such as market basket analysis, customer segmentation, and anomaly detection
Reinforcement Learning: In this paradigm, an agent learns to make decisions by performing actions and receiving rewards or penalties. It is about taking suitable action to maximize reward in a particular situation
Use-Case for Reinforcement Learning: It is widely used in areas such as robotics, gaming, navigation, and real-time decisions, where the model needs to make a sequence of decisions
Each of these learning paradigms has its unique characteristics and is suited for different types of problems in the field of artificial intelligence.

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