| parent,child |
| Machine Learning,Supervised Learning |
| Machine Learning,Unsupervised Learning |
| Machine Learning,Reinforcement Learning |
| Supervised Learning,Regression |
| Supervised Learning,Classification |
| Unsupervised Learning,Dimensionality Reduction |
| Unsupervised Learning,Clustering |
| Supervised Learning,Decision Tree |
| Regression,Decision Tree |
| Classification,Decision Tree |
| Classification,Model Usage |
| Classification,Model Construction |
| Decision Tree,Information gain |
| Decision Tree,Gini index |
| Unsupervised Learning,Eager learning |
| Unsupervised Learning,Lazy learning |
| Supervised Learning,Eager learning |
| Supervised Learning,Lazy learning |
| Classification,K Nearest Neighbour |
| Lazy learning,K Nearest Neighbour |
| Eager learning,Decision Tree |
| Supervised Learning,Neural Network |
| Eager learning,Neural Network |
| Neural Network,Gradient Descent |
| Neural Network,Multilayered Perceptron |
| Neural Network,Overfitting |
| Neural Network,Convolutional Neural Network |
| Gradient Descent,Back Propogation |
| Convolutional Neural Network,Multilayered Perceptron |
| Convolutional Neural Network,Pooling |
| Convolutional Neural Network,Flattening |
| Convolutional Neural Network,Stride |
| Clustering,K means clustering |
| Clustering,Hierarchical clustering |
| Clustering,Data similarity |
| K means clustering,Data similarity |
| Hierarchical clustering,Data similarity |
| Hierarchical clustering,Hierarchical algorithms |
| K means clustering,Partitioning algorithms |
| Overfitting,Generalization |
| Overfitting,Regularization |
| Regularization,Dropout |
| Regularization,Data Augmentation |
| Regularization,Early stopping |
| Classification,Ensemble methods |
| Data similarity,Text data |
| Text data,Word2vec |
| Data similarity,Cosine similarity |
| Word2vec,Cosine similarity |
| Unsupervised Learning,Embedding |
| Supervised Learning,Embedding |
| Embedding,Dimensionality Reduction |
| Dimensionality Reduction,Primary Component Analysis |
| Embedding,Autoencoder |
| Reinforcement Learning,Markov decision process |
| Reinforcement Learning,Value function |
| Reinforcement Learning,Quality function |
| Quality function,Q-Learning |
| Q-Learning,Exploration |
| Word2vec,Bag of words model |
| Word2vec,Skip-Gram |
| Dimensionality Reduction,Feature selection |
| Dimensionality Reduction,Feature extraction |
| Value function,Policy |
| Value function,State |
| Value function,Action |
| Value function,Reward |
| Ensemble methods,Bagging |
| Ensemble methods,Boosting |
| Ensemble methods,Random Forest |
| Decision Tree,Random Forest |
| Back Propogation,Chain rule |
| Multilayered Perceptron,Gradient Descent |
| Multilayered Perceptron,Decision Boundary |
| Convolutional Neural Network,image |
| Convolutional Neural Network,sparse connections |
| Convolutional Neural Network,parameter sharing |
| Pooling,parameters reduction |
| Pooling,Max Pooling |
| Pooling,Average Pooling |
| Convolutional Neural Network,Padding |
| Padding,Zero padding |
| Data similarity,intra-class similarity |
| Data similarity,inter-class similarity |
| Bagging,minimize variance |
| Boosting,minimize bias |
| Boosting,Adaptive Boosting |
| Boosting,XGBoost |
| Hierarchical clustering,Agglomerative Nesting |
| Text data,Inverse document frequency |
| Text data,Latent Semantic Analysis |
| Dimensionality Reduction,Single Value Decomposition |
| Primary Component Analysis,Latent Semantic |
| Single Value Decomposition,Latent Semantic |
| Bag of words model,one hot encoding |
| Bag of words model,memory consuming |
| Autoencoder,Convolutional AE |
| Autoencoder,Denoising AE |
| Autoencoder,Deep AE |
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