Glossary

**Backpropagation**: A key algorithm used in training artificial neural networks, where the model's error is calculated and propagated back through the network to adjust weights and minimize the loss function.

**Bagging**: An ensemble learning technique that improves the stability and accuracy of machine learning algorithms by training multiple versions of a model on different subsets of the data and averaging their predictions.

**Bayesian Inference**: A method of statistical inference that updates the probability estimate for a hypothesis as more evidence or information becomes available.

**Bayesian Network**: A graphical model that represents the probabilistic relationships among a set of variables using a directed acyclic graph.

**Bias (in AI)**: Systematic errors in machine learning models that arise due to assumptions made during the algorithm's creation, leading to unfair or inaccurate predictions.

**Big Data**: Extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

**Binary Classification**: A type of classification task that involves predicting one of two possible classes for a given input.

**Boosting**: An ensemble learning technique that combines multiple weak learners to form a strong learner by focusing on the errors made by previous models.

**Bots**: Automated programs that can perform tasks on the internet, such as web scraping, data entry, or customer service interactions.

**Boundary Detection**: A technique used in image processing and computer vision to identify the edges or boundaries of objects within an image.

**Brute Force Search**: A method of solving problems by trying all possible solutions until the correct one is found, often used in cryptography and optimization problems.

**Bucketing**: The process of grouping data into discrete intervals or bins, often used in data preprocessing to handle continuous variables.

**Bayesian Optimization**: A method of optimizing complex, black-box functions that are expensive to evaluate by using a probabilistic model to make decisions about where to evaluate the function next.

**Bias-Variance Tradeoff**: The balance between the error introduced by bias (error from overly simplistic models) and variance (error from overly complex models) in a machine learning model.

**Bag-of-Words (BoW)**: A representation of text data where each document is described by the frequency of words within it, ignoring grammar and word order.

**Bayesian Reasoning**: The use of Bayesian inference to update beliefs or predictions in light of new evidence, commonly used in AI for decision-making under uncertainty.

**Batch Normalization**: A technique used in training deep neural networks to normalize the inputs to each layer, speeding up training and improving stability.

**Bayesian Nonparametrics**: A branch of Bayesian statistics that allows for models with an infinite number of parameters, often used in machine learning for clustering and density estimation.

**Beam Search**: A search algorithm that explores a graph by expanding the most promising nodes in a limited set, often used in natural language processing for tasks like machine translation.

**Behavioral Cloning**: A method in reinforcement learning where the agent learns to imitate the actions of a human expert by observing their behavior.

**Bias in Data**: Refers to systematic errors in data that can lead to skewed or unfair outcomes in machine learning models.

**Binomial Distribution**: A probability distribution that summarizes the likelihood that a value will take one of two independent states across a given number of experiments.

**Boltzmann Machine**: A type of stochastic recurrent neural network that can learn a probability distribution over its set of inputs, often used in unsupervised learning tasks.

**Bootstrap Aggregating (Bagging)**: A method for improving the accuracy and robustness of models by training multiple models on random subsets of the data and aggregating their predictions.

**Bayesian Hierarchical Model**: A statistical model that considers data as being generated from multiple levels of random processes, allowing for complex dependencies and variability in the data.

**Batch Gradient Descent**: An optimization algorithm that updates model parameters by computing the gradient of the loss function over the entire dataset, as opposed to using a subset of the data.

**Binary Tree**: A data structure where each node has at most two children, commonly used in algorithms such as decision trees and binary search trees.

**Bidirectional LSTM (BiLSTM)**: A type of recurrent neural network that processes data in both forward and backward directions, capturing dependencies from both past and future contexts in sequence data.

**Big-O Notation**: A mathematical notation used to describe the upper bound of an algorithm's time or space complexity, giving an idea of the worst-case scenario as the input size grows.

**Boosting Trees**: An ensemble learning method that builds models sequentially, each new model attempting to correct the errors made by the previous ones, often used in algorithms like XGBoost.

**Backpropagation Through Time (BPTT)**: A variant of backpropagation used for training recurrent neural networks, where the algorithm unfolds the network through time to compute gradients for the entire sequence.

**Bootstrap Sampling**: A statistical method that involves repeatedly sampling with replacement from a dataset to estimate the sampling distribution of a statistic.

**Bias-Variance Decomposition**: The process of decomposing the error of a model into bias, variance, and irreducible error components, helping to understand the model's performance.

**Bayes' Theorem**: A mathematical formula used to calculate conditional probabilities, forming the foundation of Bayesian inference.

**Bidirectional Encoder Representations from Transformers (BERT)**: A transformer-based model designed to pre-train deep bidirectional representations by conditioning on both left and right context in all layers, widely used in NLP tasks.

**Bellman Equation**: A fundamental recursive equation in dynamic programming that breaks down the decision-making process into smaller, manageable subproblems, widely used in reinforcement learning.

**Binary Cross-Entropy**: A loss function used in binary classification tasks that measures the difference between the predicted probabilities and the actual labels.

**Bias (Data Science)**: The systematic deviation from the true value, often leading to models that are consistently wrong in the same direction, typically due to assumptions made during model development.

**Bayesian Classification**: A statistical classification method based on Bayes' theorem, often used when the features are conditionally independent given the class.

**Binary Logistic Regression**: A statistical method used to model the probability of a binary outcome based on one or more predictor variables.

**Bayesian Information Criterion (BIC)**: A criterion for model selection among a finite set of models; it is based on the likelihood function and penalizes the number of parameters to prevent overfitting.

**Backpropagation (Neural Networks)**: The process of training a neural network by propagating the error backward from the output layer to the input layer to update the weights of the network.

**Bayesian Deep Learning**: A branch of deep learning that incorporates Bayesian methods to quantify uncertainty in model predictions, often leading to more robust and interpretable models.

**Bias Correction**: Techniques used to adjust a biased estimator in order to make it more accurate or to remove the bias entirely.

**Batch Processing**: The execution of a series of jobs in a program on a computer without manual intervention, often used in data processing and analysis tasks.

**Bayesian Optimization**: A probabilistic model-based optimization technique that is particularly useful for optimizing expensive black-box functions with a small number of evaluations.

**Binary Search**: An efficient algorithm for finding an item from a sorted list of items, working by repeatedly dividing the search interval in half.

**Backoff Model**: A type of n-gram language model used in natural language processing, which estimates the probability of a word based on smaller n-grams if the larger n-gram has zero occurrences.

**Bootstrap Sampling (Data Science)**: A method used to estimate the distribution of a statistic by resampling with replacement from the original dataset and calculating the statistic on each resample.

**Beam Search (NLP)**: A search algorithm that explores a graph by expanding the most promising nodes, often used in sequence generation tasks like machine translation and speech recognition.

**Bellman Operator**: A key operator in dynamic programming and reinforcement learning that maps a value function to itself, helping to find the optimal policy.

**Binary Heap**: A complete binary tree where each node is greater than or equal to (max heap) or less than or equal to (min heap) each of its children, commonly used to implement priority queues.

**Bayesian Neural Networks (BNN)**: Neural networks that use Bayesian inference to estimate distributions over the network's parameters, allowing for the modeling of uncertainty in predictions.

**Bayesian Hierarchical Models**: Statistical models that involve multiple levels of random variables, often used to model complex data structures where the data is organized at more than one level.

**Bagging (Bootstrap Aggregation)**: An ensemble method that combines the predictions of multiple models trained on different random subsets of the data, reducing variance and improving accuracy.

**Binary Variable**: A variable that takes on one of two possible values, often used in binary classification tasks where the goal is to predict one of two classes.

**Bayesian Nonparametric Models**: Models that allow for an infinite number of parameters, enabling the model to grow in complexity as more data is observed, often used in clustering and density estimation.

**Backpropagation (RNN)**: The method used to train recurrent neural networks, where errors are propagated backward through time to update the weights of the network.

**Bayesian Inference (Machine Learning)**: The process of updating the probability distribution of a model's parameters based on observed data, using Bayes' theorem.

**Batch Size**: The number of training examples utilized in one iteration of model training, affecting the speed and stability of training in machine learning.

**Bias (Model Performance)**: The tendency of a model to consistently predict certain outcomes over others, leading to systematic errors that reflect the assumptions made during model development.

**Bayesian Updating**: The process of updating the probability estimate for a hypothesis as more evidence or information becomes available