Statistics and Machine Learning Overview

These flashcards cover key concepts from statistics and machine learning, including definitions and explanations of methods, metrics, and algorithms.

Descriptive Statistics

Statistics that summarize or describe characteristics of a data set.

Central Tendency Measures

Metrics that indicate the central point of a data set, including mean, median, and mode.

Variation Measures

Metrics that describe the spread of data, including standard deviation, range, and variance.

Hypothesis Testing

A method in inferential statistics used to determine if there is evidence to support a hypothesis.

Covariance

A measure that indicates the extent to which two variables change together.

Correlation Coefficient

A numerical measure that describes the size and direction of a relationship between two variables.

Causation

A statistical concept indicating that one event is the result of another event.

Probability Theory

A branch of mathematics that deals with the likelihood of events occurring.

Bayesian Statistics

A statistical approach that incorporates prior knowledge into the analysis of data.

Machine Learning

A branch of artificial intelligence that uses algorithms to find patterns in data.

Supervised Learning

A machine learning type where the model is trained on labeled data.

Unsupervised Learning

A machine learning type that deals with unlabeled data to find patterns.

Semi-Supervised Learning

A learning paradigm that utilizes both labeled and unlabeled data for training.

Regression

A statistical method for predicting a continuous outcome variable based on one or more independent variables.

Classification

A machine learning task where the goal is to assign categories to input data.

K-Nearest Neighbors (K-NN)

A classification algorithm that assigns the class of a data point based on the classes of its K nearest neighbors.

Support Vector Machine (SVM)

A supervised learning model used for classification that finds the optimal hyperplane to separate data into classes.

Decision Trees

A model that uses a tree-like graph of decisions to classify data points.

Artificial Neural Networks (ANNs)

Computational models inspired by the human brain, used for recognizing patterns and solving complex problems.

Clustering

A method in unsupervised learning that groups data points into clusters based on similarity.

K-Means Clustering

An algorithm that divides N data records into K clusters by minimizing variance.

Agglomerative Clustering

A hierarchical clustering method that merges clusters based on distance criteria.