Here are the theoretical differences between supervised, unsupervised, and reinforcement learning: 1. Supervised Learning: Definition: A type of machine learning where the model is trained on a labeled dataset, meaning each training example is paired with an output label. Objective: To learn a mappiRead more
Here are the theoretical differences between supervised, unsupervised, and reinforcement learning:
1. Supervised Learning:
Definition: A type of machine learning where the model is trained on a labeled dataset, meaning each training example is paired with an output label.
Objective: To learn a mapping from inputs to outputs so that the model can predict the output for new, unseen inputs.
Key Concepts:
Training Data: Consists of input-output pairs.
Loss Function: Measures the difference between the model’s predictions and the actual outputs. The goal is to minimize this loss.
2. Unsupervised Learning:
Definition: A type of machine learning where the model is trained on a dataset without labeled responses. The goal is to find hidden patterns or intrinsic structures in the input data.
Objective: To learn the underlying structure of the data without explicit guidance on what the output should be.
Key Concepts:
Clustering: Grouping similar data points together. Examples include K-means and hierarchical clustering.
Anomaly Detection: Identifying rare items, events, or observations which raise suspicions by differing significantly from the majority of the data.
3. Reinforcement Learning:
Definition: A type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize some notion of cumulative reward.
Objective: To learn a policy that maps states of the environment to the actions the agent should take to maximize the expected reward over time.
Key Concepts:
Agent: The learner or decision maker.
Environment: The external system the agent interacts with.
State: A representation of the current situation of the environment.
Action: A set of all possible moves the agent can make.
Reward: Immediate return received after taking an action.
Policy: A strategy used by the agent to decide the next action based on the current state.
These concepts form the foundation of their respective learning paradigms and guide the development of various machine learning algorithms and applications.
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