AI (Artificial Intelligence): The capability of machines to perform complex tasks by imitating human cognitive functions such as learning, understanding, and problem-solving.

Machine Learning: A subset of AI that allows computers to learn and make decisions from data without being explicitly programmed.

Data Science: An interdisciplinary field that uses various techniques, algorithms, and systems to extract insights and knowledge from structured and unstructured data.

Bayesian Bandit: A probabilistic approach to the multi-armed bandit problem that uses Bayes’ theorem to update the probability distribution of each arm’s reward.

Multi-Arm Bandit: A problem in probability theory and statistics where a gambler has to decide which arm of a K-slot machine to pull to maximize his total reward over a series of trials.

Random Forest: An ensemble learning method that creates a ‘forest’ of decision trees during training and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.

Clustering: A type of unsupervised machine learning where data points are grouped into clusters based on their similarities, without prior knowledge of these groupings.

Supervised Learning: A machine learning task where a model is trained on labeled data, meaning each example in the training dataset is paired with the correct output.