A branch of mathematics concerning vector spaces and linear mappings, essential for understanding data transformations in ML.

Mathematics focusing on change and motion, crucial for optimizing algorithms and understanding gradients in ML.

The study of randomness and uncertainty, foundational for modeling and making predictions in machine learning.

Principles and methods for collecting, analyzing, and interpreting data, vital for validating ML models.

A step-by-step procedure or formula for solving a problem, central to machine learning implementations.

A type of ML where models are trained on labeled data to make predictions.

ML where models identify patterns in unlabeled data without explicit outputs.

When a model learns noise instead of the underlying pattern, leading to poor generalization.

When a model is too simple to capture the underlying trend, resulting in poor performance.

A technique for assessing how the results of a statistical analysis will generalize to an independent dataset.

Parameters set before the learning process begins, affecting model training and performance.

Techniques to prevent overfitting by adding a penalty to the loss function.

An optimization algorithm for minimizing the loss function by iteratively moving towards the steepest descent.

A table used to evaluate the performance of a classification algorithm, showing true vs. predicted classifications.

The ratio of true positive predictions to the total predicted positives, measuring accuracy in positive predictions.

The ratio of true positive predictions to the total actual positives, measuring the ability to find all relevant instances.

The harmonic mean of precision and recall, providing a balance between the two metrics.

The process of selecting, modifying, or creating features to improve model performance.

Techniques to reduce the number of features in a dataset while preserving important information.

Techniques that combine multiple models to improve performance and robustness.

Computational models inspired by the human brain, used for complex pattern recognition tasks.

A subset of ML using neural networks with multiple layers to model complex data representations.

A method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence becomes available.

The task of grouping a set of objects in such a way that objects in the same group are more similar than those in other groups.

The steps taken to clean and prepare raw data for analysis, crucial for effective ML.

Practical uses of machine learning techniques to solve significant problems across various industries.