Supervised, Unsupervised, and Reinforcement Learning

Aspect	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Definition	A type of learning where the model is trained on a labeled dataset, which means that the training data includes both input data and the corresponding correct outputs.	Learning from an unlabeled dataset, the model tries to find the underlying patterns and structures in the data.	A type of learning where the model learns to interact with an environment to achieve a goal or maximize some notion of cumulative reward.
Training Data	Labeled data (features and labels)	Unlabeled data (features only)	Interaction with the environment, rewards based on actions.
Goal	To make accurate predictions or classifications based on the input data.	To find hidden patterns or groupings in the data.	To find a strategy to obtain the maximum cumulative reward over time.
Algorithms	Decision Trees, Support Vector Machines, Neural Networks, etc.	Clustering (e.g., K-means), Association (e.g., Apriori), Principal Component Analysis, etc.	Q-learning, Deep Q Network (DQN), Policy Gradients, etc.
Real-world Applications	Image recognition, Spam detection, Credit risk analysis, etc.	Market segmentation, Anomaly detection, Recommender systems, etc.	Autonomous vehicles, Game playing (like AlphaGo), Robotics, etc.
Evaluation Metrics	Accuracy, Precision, Recall, F1-score, etc.	Silhouette score, Davies-Bouldin index, etc.	Reward function, which may vary greatly depending on the specific task.

Common Algorithms

Supervised Learning	Unsupervised Learning	Reinforcement Learning
Linear Regression	K-Means Clustering	Q-Learning
Logistic Regression	Hierarchical Clustering	Deep Q-Network (DQN)
Decision Trees	DBSCAN	Policy Gradients
Support Vector Machines (SVM)	Gaussian Mixture Models (GMM)	Actor-Critic Methods
Neural Networks	Principal Component Analysis (PCA)	Proximal Policy Optimization (PPO)
Naïve Bayes	Independent Component Analysis (ICA)	Monte Carlo Tree Search (MCTS)
k-Nearest Neighbors (k-NN)	t-SNE	SARSA
Gradient Boosting Machines (GBM)	Latent Dirichlet Allocation (LDA)	Temporal Difference Learning (TD Learning)
Random Forests	Association Rules (Apriori, FP-Growth)	Trust Region Policy Optimization (TRPO)