Define supervised, unsupervised, and reinforcement learning.

Supervised Learning

Definition: Supervised learning is a type of machine learning where the model is trained on a labeled dataset. Each training example is paired with an output label, and the algorithm learns to map inputs to outputs.

Key Characteristics:

• Training Data: Consists of input-output pairs (e.g., images with labels, text with categories).
• Objective: Learn a mapping function that can predict the output for new, unseen inputs.
• Common Algorithms: Linear regression, logistic regression, support vector machines, decision trees, and neural networks.

Examples:

• Classification: Email spam detection (classifying emails as spam or not spam).
• Regression: Predicting house prices based on features like size and location.

2. Unsupervised Learning

Definition: Unsupervised learning is a type of machine learning where the model is trained on an unlabeled dataset. The algorithm tries to find hidden patterns or intrinsic structures in the data without predefined labels.

Key Characteristics:

• Training Data: Consists only of input data without corresponding output labels.
• Objective: Discover the underlying structure or distribution in the data.
• Common Algorithms: K-means clustering, hierarchical clustering, principal component analysis (PCA), and association rule learning.

Examples:

• Clustering: Grouping customers based on purchasing behavior without prior knowledge of customer segments.
• Dimensionality Reduction: Reducing the number of features in a dataset while preserving its variance (e.g., using PCA).

3. Reinforcement Learning

Definition: Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards. The agent learns through trial and error, receiving feedback in the form of rewards or penalties.

Key Characteristics:

• Training Data: Consists of interactions with an environment, including actions taken, rewards received, and state transitions.
• Objective: Learn a policy that maximizes the long-term cumulative reward.
• Common Algorithms: Q-learning, deep Q-networks (DQN), policy gradients, and actor-critic methods.

Examples:

• Game Playing: Training an agent to play games like chess or Go by receiving rewards for winning moves and penalties for losing moves.
• Robotics: Teaching a robot to navigate a maze or perform complex tasks through trial and error.