Domain adaptation (machine learning)
Domain adaptation is a subfield of machine learning focused on adapting models trained on one domain to perform well on a different but related domain.
data science
Overfitting
Overfitting is a modeling error in machine learning and statistics where a model captures noise or random fluctuations in training data rather than the underlying pattern. This leads to poor generalization to new, unseen data.
Supervised learning
Supervised learning is a type of machine learning where models are trained on labeled data to make predictions or classify new data. It involves using input-output pairs to guide the learning process.
Self-supervised learning
Self-supervised learning is a machine learning paradigm that uses unlabeled data to train models, allowing them to learn useful representations without extensive human annotation.
Semi-supervised learning
Semi-supervised learning is a machine learning approach that uses both labeled and unlabeled data to improve model accuracy, bridging the gap between supervised and unsupervised learning.
Causality in AI
Causality in AI refers to the understanding and modeling of cause-and-effect relationships within artificial intelligence systems.
Sub-symbolic AI
Sub-symbolic AI refers to a category of artificial intelligence that operates without explicit symbolic representation of knowledge, relying instead on connectionist or statistical methods.
Graph attention network (GAT)
Graph Attention Networks (GAT) are a type of neural network architecture designed to operate on graph-structured data, utilizing attention mechanisms to weigh node relationships.
Hyperparameter optimization
Hyperparameter optimization is a crucial process in machine learning that involves tuning the parameters of a model to improve its performance.
Gradient boosting
Gradient boosting is a machine learning technique used for regression and classification tasks, known for its predictive accuracy and flexibility.