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27 lines
3.4 KiB
Markdown
27 lines
3.4 KiB
Markdown
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# Supervised, Unsupervised, and Reinforcement Learning
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| Aspect | Supervised Learning | Unsupervised Learning | Reinforcement Learning |
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|-----------------------------|--------------------------------------------|--------------------------------------------|-------------------------------------------|
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| 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. |
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| Training Data | Labeled data (features and labels) | Unlabeled data (features only) | Interaction with the environment, rewards based on actions. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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## Common Algorithms
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| Supervised Learning | Unsupervised Learning | Reinforcement Learning |
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|--------------------------------------------|--------------------------------------------|------------------------------------------|
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| Linear Regression | K-Means Clustering | Q-Learning |
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| Logistic Regression | Hierarchical Clustering | Deep Q-Network (DQN) |
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| Decision Trees | DBSCAN | Policy Gradients |
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| Support Vector Machines (SVM) | Gaussian Mixture Models (GMM) | Actor-Critic Methods |
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| Neural Networks | Principal Component Analysis (PCA) | Proximal Policy Optimization (PPO) |
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| Naïve Bayes | Independent Component Analysis (ICA) | Monte Carlo Tree Search (MCTS) |
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| k-Nearest Neighbors (k-NN) | t-SNE | SARSA |
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| Gradient Boosting Machines (GBM) | Latent Dirichlet Allocation (LDA) | Temporal Difference Learning (TD Learning)|
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| Random Forests | Association Rules (Apriori, FP-Growth) | Trust Region Policy Optimization (TRPO) |
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