Fundamental Mathematics and Statistics

These terms highlight the core mathematical and statistical concepts that underpin the theory and practice of artificial intelligence and machine learning, providing the foundation for developing algorithms and understanding their behavior.

Accuracy - A statistical measure of how well a binary classification test correctly identifies or excludes a condition, fundamental in evaluating model performance.

Activation Function - A mathematical equation that determines the output of a neural network node given an input or set of inputs, crucial for introducing non-linearity into the model.

Association - Refers to rules or patterns that describe how variables within a dataset relate to each other, based on principles of correlation and causation in statistics.

Classification - A problem where the output variable is a category, such as "spam" or "not spam", involving statistical methods for assigning inputs into categories.

Clustering - A statistical method used to group a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups.

Cross-Validation - A statistical method used to estimate the skill of machine learning models. It divides the data into subsets, trains the model on some subsets and validates it on others.

Dimensionality Reduction - A process of reducing the number of random variables under consideration, by obtaining a set of principal variables. Techniques like PCA (Principal Component Analysis) are foundational statistical methods used here.

Error Minimization - Involves mathematical optimization techniques to adjust the parameters of a model to minimize the difference between the predicted and actual values.

Evaluation Metric - Statistical measures used to assess the performance of a model or algorithm, including precision, recall, F1 score, and others.

Loss Function - A mathematical function that quantifies the difference between the expected outcomes and the outcomes predicted by the model, guiding the optimization process in machine learning.

Markov Decision Process (MDP) - A mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision-maker.

Policy or Q-function - In reinforcement learning, a function that represents the expected return of taking an action in a given state, following a certain policy, grounded in the mathematics of decision processes.

Regression - A statistical method for estimating the relationships among variables. It's fundamental in predicting a continuous-valued attribute associated with an object.

Reward Signal - In reinforcement learning, a scalar feedback signal that indicates how well an action taken by an agent is at achieving the goal, central to the statistical concept of reward maximization.

Value Function - In the context of reinforcement learning, a function that estimates how good a particular state is for an agent to be in, based on expected future rewards, embodying principles of predictive modeling and expectation in statistics.