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An iterative optimization algorithm used in machine learning to find the minimum of a function by moving in the direction of the steepest descent as defined by the negative of the gradient.

Occurs when a model learns the training data too well, capturing noise along with the underlying pattern, thus performing poorly on unseen data.

Supervised learning models that analyze data used for classification and regression analysis by finding the hyperplane that best divides a dataset into classes.

The process of selecting a subset of relevant features for use in model construction, reducing dimensionality, and improving model performance.

A statistical method used to estimate the skill of machine learning models by dividing the data into subsets and testing the model on each subset.

A class of deep neural networks commonly applied to analyzing visual imagery, using a series of convolutional layers for feature extraction and pattern recognition.

An ensemble learning method for classification and regression that operates by constructing multiple decision trees during training and outputting the mode or mean prediction.

A statistical procedure that uses orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.

A type of unsupervised learning algorithm that groups data into K number of sets by having each point belong to the cluster with the nearest mean.

A category of machine learning that involves training models on data without labeled outcomes, focusing on finding patterns or structures in the data.

A technique used to prevent overfitting by adding a penalty term to the loss function or by artificially augmenting the dataset (e.g., L1, L2 regularization).

Techniques that create multiple models and combine them to produce improved results, such as Random Forest, Boosting, and Bagging.

A special kind of recurrent neural network (RNN) capable of learning long-term dependencies, LSTMs are widely used for sequence prediction problems.

A type of machine learning where models are trained using labeled data, meaning the training data includes the desired outputs.

A type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve some notion of cumulative reward.

Happens when a model is too simple to capture the underlying trend in the data, resulting in poor performance both on training and new data.

A non-parametric supervised learning method used for classification and regression that models decisions and possible consequences as a tree-like structure.

A set of algorithms modeled after the human brain that are designed to recognize patterns and interpret sensory data through a kind of machine perception.

A method used to normalize the range of independent variables or features of data, often used in the preprocessing stage.