A comparison of supervised, unsupervised, and reinforcement learning :
Supervised Learning :
* In supervised learning, the algorithm is trained on a labeled dataset, where each example consists of input-output pairs.
* The goal is to learn a mapping from inputs to outputs, such that the algorithm can predict the correct output for new, unseen inputs.
* The learning process involves minimizing a loss function that measures the difference between the predicted output and the true output.
* Examples of supervised learning tasks include classification (e.g., spam detection, image recognition) and regression (e.g., predicting house prices, stock prices).
Unsupervised Learning :
* In unsupervised learning, the algorithm is trained on an unlabeled dataset, where only input data is provided without corresponding output labels.
* The goal is to find patterns, structure, or relationships within the data without explicit guidance.
* Common tasks in unsupervised learning include clustering (grouping similar data points together), dimensionality reduction (reducing the number of features while preserving information), and density estimation (estimating the probability distribution of the data).
Reinforcement Learning :
* In reinforcement learning, an agent learns to make sequential decisions by interacting with an environment.
* The agent receives feedback in the form of rewards or penalties based on its actions, but no explicit supervision is provided on which actions to take.
* The goal is to learn a policy that maximizes cumulative rewards over time.
* Reinforcement learning involves learning from trial and error, with the agent exploring different actions and learning from the consequences of its actions.
* Examples of reinforcement learning applications include game playing (e.g., chess, Go), robotic control, recommendation systems, and autonomous driving.
