Glossary

**Early Stopping**: A technique used during training of machine learning models to halt the training process when performance on a validation set begins to degrade, preventing overfitting.

**Edge Computing**: A distributed computing paradigm that brings computation and data storage closer to the location where it is needed, improving response times and saving bandwidth.

**Edge Detection**: A technique used in image processing to identify the boundaries within an image by detecting discontinuities in brightness.

**Eigendecomposition**: The process of decomposing a matrix into its eigenvalues and eigenvectors, often used in dimensionality reduction techniques such as Principal Component Analysis (PCA).

**Elastic Net**: A regularization technique that combines both L1 and L2 penalties of the lasso and ridge methods to improve the model's performance, especially in cases of multicollinearity.

**Embedding**: A representation of data in a lower-dimensional space, often used in natural language processing to convert words or phrases into vectors of real numbers.

**Ensemble Learning**: A machine learning approach that combines the predictions of multiple models to produce a more accurate and robust final prediction.

**Entropy**: A measure of uncertainty or disorder, often used in information theory to quantify the unpredictability of a random variable or the impurity in decision trees.

**Episode**: In reinforcement learning, an episode is a single run of the agent through the environment, starting from an initial state and ending when a terminal state is reached.

**Epoch**: A full pass through the entire training dataset during the training of a machine learning model, often involving multiple epochs to optimize model performance.

**Equivariance**: A property of a model where applying a transformation to the input results in a corresponding transformation of the output, important in models like convolutional neural networks.

**Error Rate**: The proportion of incorrect predictions made by a machine learning model, typically measured on a validation or test set.

**Evolutionary Algorithms**: A class of optimization algorithms inspired by the process of natural selection, where candidate solutions evolve over time to optimize a specific objective function.

**Exponential Decay**: A technique in learning rate scheduling where the learning rate decreases exponentially as training progresses, helping the model converge more smoothly.

**Exploratory Data Analysis (EDA)**: An approach to analyzing data sets to summarize their main characteristics, often using visual methods to uncover patterns, spot anomalies, and check assumptions.

**Exploitation vs. Exploration**: A dilemma in reinforcement learning where the agent must decide between exploiting known rewards and exploring new actions to discover potentially better rewards.

**Exponential Moving Average (EMA)**: A time series forecasting method that applies exponentially decreasing weights to past observations, smoothing out data to reveal trends.

**Expert System**: A type of AI program that mimics the decision-making abilities of a human expert, typically using a set of rules derived from expert knowledge.

**Extrapolation**: The process of estimating beyond the original observation range, using the trends observed in the data to predict future outcomes.

**Expectation-Maximization (EM) Algorithm**: An iterative method used to find the maximum likelihood estimates of parameters in statistical models, especially when data is incomplete or has latent variables.

**Explained Variance**: A measure of how much of the total variance in the data is captured by the model, often used in the context of Principal Component Analysis (PCA).

**Exploratory Factor Analysis (EFA)**: A statistical technique used to identify underlying relationships between measured variables by modeling the data in terms of a smaller number of unobserved factors.

**Explicit Feedback**: Direct feedback provided by users, such as ratings or likes, often used in recommendation systems to learn user preferences.

**Empirical Risk Minimization (ERM)**: A principle in machine learning that aims to minimize the average loss on the training data, guiding the selection of model parameters.

**Entity Embedding**: The process of mapping categorical variables into a continuous vector space, commonly used in deep learning to handle high-cardinality categorical features.

**Ensembling**: The process of combining predictions from multiple machine learning models to create a more accurate and stable prediction, often through methods like bagging, boosting, or stacking.

**Epsilon-Greedy**: A strategy used in reinforcement learning to balance exploration and exploitation by choosing a random action with probability epsilon, and the best-known action with probability 1-epsilon.

**Encoder-Decoder Model**: A neural network architecture commonly used in sequence-to-sequence tasks, where the encoder processes the input into a fixed-length vector and the decoder generates the output sequence.

**End-to-End Learning**: A learning approach where a model is trained to perform a task directly from input data to output predictions, without breaking the problem into intermediate steps or stages.

**Entropy Regularization**: A technique used in machine learning to encourage a model to produce more uncertain or diverse predictions, often applied in reinforcement learning to promote exploration.

**Edge Detection**: A method in image processing and computer vision that identifies points in a digital image where the image brightness changes sharply, often used for feature extraction.

**Evolutionary Strategy (ES)**: A type of evolutionary algorithm focused on optimizing real-valued parameters, often used in continuous optimization problems.

**Event-Driven Architecture (EDA)**: A software architecture pattern promoting the production, detection, consumption, and reaction to events, often used in systems requiring real-time processing.

**Embedding Space**: The multidimensional space in which embeddings, such as word vectors or image features, are represented, allowing for operations like similarity searches or clustering.

**Explicit Model**: A model where the functional form of the relationship between variables is fully specified, in contrast to implicit models where the relationship is defined indirectly or inferred from data.

**Edge List**: A data structure used in graph theory to represent a graph by listing its edges, often used to store and analyze network data.

**Empirical Distribution**: The distribution of a set of observations, often used to estimate the probability distribution of a population based on a finite sample.

**Entropic Regularization**: A technique used to smooth optimization problems by adding an entropy term to the objective function, promoting exploration and diversity in the solutions.

**Exploding Gradient Problem**: A challenge in training deep neural networks where gradients grow exponentially during backpropagation, leading to instability and divergence of the model parameters.

**Edge Case**: A problem or situation that occurs only at an extreme operating parameter, often used to test the robustness of machine learning models and algorithms.

**Expectation Propagation**: A variational inference method used in Bayesian statistics to approximate complex distributions, offering a trade-off between accuracy and computational efficiency.

**Expert Annotator**: A human expert who labels or annotates data for use in machine learning models, often used in domains requiring specialized knowledge.

**Exposure Bias**: A problem in sequence generation models where the model is trained on ground-truth sequences but tested on sequences generated by the model itself, leading to errors that accumulate over time.

**Entity Recognition**: A task in natural language processing where named entities, such as people, organizations, or locations, are identified and classified within text.

**Elastic Distortion**: A data augmentation technique often used in image processing where small, random distortions are applied to the image to make the model more robust to variations.

**Encoder**: The part of a neural network model, particularly in sequence-to-sequence tasks, that processes the input data into a fixed-length vector or set of vectors representing the information contained in the input.

**Ensemble Averaging**: A method in ensemble learning where the predictions of multiple models are averaged to produce a final prediction, often reducing variance and improving generalization.

**Equilibrium Point**: In game theory and optimization, a point where no participant can improve their outcome by changing their strategy, often used to analyze the stability of systems.

**Exploitability**: In reinforcement learning, the extent to which an agent's strategy can be exploited by an opponent, often considered when designing robust agents.

**Erosion**: A morphological operation in image processing that shrinks the boundaries of regions of foreground pixels, often used to remove small noise in binary images.

**Exponential Smoothing**: A time series forecasting method that applies exponentially decreasing weights to past observations, emphasizing more recent data points while smoothing out fluctuations.

**Exact Match Ratio**: A metric used to evaluate models, particularly in tasks like question answering or information retrieval, by measuring the proportion of predictions that exactly match the ground truth.

**Epistemic Uncertainty**: Uncertainty in machine learning models due to limited knowledge or data, often reducible with more data or better models, in contrast to aleatoric uncertainty, which is inherent and irreducible.

**Ensemble Diversity**: The degree of variation among the models in an ensemble, where greater diversity can lead to better overall performance due to complementary strengths and weaknesses of the individual models.

**Eigenvector**: A vector that remains unchanged in direction after a linear transformation, associated with a particular eigenvalue, often used in dimensionality reduction and principal component analysis.

**Error Backpropagation**: The process of propagating the error gradient backward through a neural network to update the weights, enabling the network to learn from its mistakes and improve accuracy.