| Feature engineering | |
| Attention mechanism | |
| Singular value decomposition | |
| Softmax function | |
| Adversarial example | |
| Adaboost | |
| Activation function | |
| Neural network | |
| Multi-task learning | |
| Loss function | |
| Image segmentation | |
| Inference | |
| Unsupervised learning | |
| Adversarial attack | |
| Cross-validation | |
| Convolutional neural network | |
| Bias-variance tradeoff | |
| Semi-supervised learning | |
| Hyperparameter | |
| Transfer learning | |
| Ensemble learning | |
| Deep learning | |
| Instance-based learning | |
| Alpha | |
| Data augmentation | |
| Weight initialization | |
| Support vector machine | |
| Evolutionary algorithm | |
| Learning rate | |
| Bag of words | |
| Precision and recall | |
| Zero-shot learning | |
| Autoencoder | |
| Backpropagation | |
| Reinforcement learning | |
| Active learning | |
| Feedforward neural network | |
| Gradient descent | |
| Bayesian optimization | |
| Label | |
| Linear regression | |
| Overfitting | |
| Variational autoencoder | |
| Embedding | |
| Artificial intelligence | |
| K-nearest neighbors | |
| Capsule network | |
| Federated learning | |
| Principal component analysis | |
| Time series analysis | |
| Dropout | |
| Clustering | |
| Fine-tuning | |
| Decision tree | |
| Regression analysis | |
| Validation | |
| Sampling | |
| Machine learning | |
| Data preprocessing | |