**Objective Function**: A function that a machine learning algorithm aims to optimize during training, typically by minimizing or maximizing it, such as the loss function in regression or classification tasks.

**One-Hot Encoding**: A technique used to represent categorical variables as binary vectors, where each category is represented by a vector with a single high (1) value and all other positions low (0).

**One-vs-All (OvA)**: A strategy used in multi-class classification where a separate binary classifier is trained for each class, treating that class as positive and all other classes as negative.

**Online Learning**: A machine learning approach where the model is updated incrementally as new data arrives, allowing it to adapt to changes over time, often used in streaming data scenarios.

**Ordinal Encoding**: A technique for converting categorical data into numerical values based on the order of the categories, often used when the categories have a meaningful ranking.

**Overfitting**: A situation where a machine learning model learns to perform well on the training data by memorizing it, but fails to generalize to new, unseen data, resulting in poor performance on test data.

**Optimization Algorithm**: A method used to adjust the parameters of a machine learning model in order to minimize or maximize the objective function, such as gradient descent or genetic algorithms.

**Outlier Detection**: The process of identifying and possibly removing data points that significantly differ from the majority of the data, often indicating errors or rare events.

**Output Layer**: The final layer in a neural network that produces the predictions or outputs of the model, typically using an activation function appropriate for the task, such as softmax for classification.

**Overlapping Clusters**: In clustering analysis, when clusters are not clearly separated and data points may belong to multiple clusters or lie near the boundary of clusters, making classification more challenging.

**One-Class SVM**: A type of support vector machine used for anomaly detection, where the model is trained on data from a single class and identifies outliers as points that do not conform to this class.

**Ordinal Regression**: A type of regression analysis used for predicting an ordinal variable, where the categories have a meaningful order but the intervals between them are not necessarily equal.

**Overlapping Generations Model (OLG)**: A type of economic model that considers multiple overlapping generations of agents, often used in macroeconomic analysis to study issues like savings, investment, and capital accumulation.

**Out-of-Bag Error (OOB Error)**: An estimate of the prediction error of a random forest or other ensemble models, calculated using the samples that were not included in the bootstrap sample for each tree.

**Ontology**: A structured framework for organizing information, often used in artificial intelligence to represent knowledge, including the relationships between concepts within a domain.

**One-vs-One (OvO)**: A strategy used in multi-class classification where a binary classifier is trained for each pair of classes, resulting in multiple classifiers whose outputs are combined to make a final decision.

**Outlier Score**: A metric used to quantify how much a data point differs from the rest of the data, with higher scores indicating a greater likelihood of the point being an outlier.

**Overlapping Distribution**: In statistics and machine learning, when the distributions of two or more classes overlap significantly, making it difficult for a model to distinguish between the classes.

**Orthogonalization**: The process of making vectors orthogonal (perpendicular) to each other, often used in feature selection or dimensionality reduction to ensure that features are uncorrelated.

**One-Pass Algorithm**: An algorithm that processes data in a single pass, without requiring the data to be stored in memory, often used in streaming or online learning scenarios.

**Optimal Transport**: A mathematical theory that deals with the most efficient ways of transporting mass between distributions, often used in machine learning for tasks like domain adaptation and distribution matching.

**Open Set Recognition**: A classification problem where the model must recognize when an input belongs to an unknown class, not seen during training, and either reject the input or classify it as "unknown."

**Out-of-Distribution (OOD) Detection**: The task of identifying when a model is presented with data that is outside the distribution it was trained on, often used to improve the reliability of machine learning models.

**Ordered Logit Model**: A type of regression model used for predicting an ordinal dependent variable, similar to logistic regression but designed for ordered categories.

**Observation Space**: The set of all possible observations that an agent can make in a given environment, often used in reinforcement learning to describe what the agent perceives at each step.

**OpenAI**: A research organization and technology company focused on developing and deploying artificial intelligence for the benefit of humanity, known for creating large-scale language models like GPT.

**Online Inference**: The process of making predictions in real-time as new data becomes available, often used in applications where decisions must be made immediately, such as fraud detection or recommendation systems.

**Open Source Software**: Software that is released with a license that allows anyone to view, modify, and distribute the source code, often used in the machine learning community to share models, tools, and frameworks.

**One-Shot Learning**: A learning task where the model is trained to recognize objects or patterns from a single example, often used in situations where data is scarce.

**Overparameterization**: A scenario in machine learning where the model has more parameters than necessary, which can lead to overfitting but may also allow the model to learn more complex patterns.

**Ordinal Classification**: A type of classification task where the labels have a natural order, but the intervals between labels are not necessarily equal, such as ranking problems.

**Optimistic Initialization**: A strategy in reinforcement learning where the initial values of the Q-table or other value functions are set higher than the expected rewards, encouraging exploration.

**On-Policy Learning**: A reinforcement learning approach where the agent learns the value of the policy it is currently using, as opposed to off-policy learning where the agent learns the value of a different policy.

**Outlier Robustness**: The ability of a machine learning model to maintain performance in the presence of outliers, often achieved through the use of robust statistics or regularization techniques.

**Order of Convergence**: A measure of how quickly an iterative algorithm converges to a solution, often used in numerical optimization to compare the efficiency of different algorithms.

**Optimization Landscape**: The graphical representation of the objective function in optimization, showing how the function value changes with respect to the parameters, often analyzed to understand the behavior of optimization algorithms.

**Open-Ended Learning**: A learning process where the model is continually exposed to new data and tasks, allowing it to learn and adapt over time without a predefined endpoint.

**Optimal Substructure**: A property of an optimization problem where the optimal solution can be constructed from optimal solutions to its subproblems, often used in dynamic programming.

**Orthogonal Matching Pursuit (OMP)**: A greedy algorithm used in signal processing and compressed sensing to find sparse solutions to linear systems by iteratively selecting the best matching components.