6 docs tagged with "discrete-math"

Understanding the foundations of binary outcomes: The Bernoulli trial and the Binomial distribution, essential for classification models.

Mastering permutations, combinations, and counting principles essential for understanding probability, feature engineering, and model complexity.

Exploring the fundamentals of graph theory, including nodes, edges, adjacency matrices, and their applications in neural networks and Knowledge Graphs.

Exploring propositional logic, logical operators, and Boolean algebra as the basis for decision-making algorithms and binary classification.

Understanding the Poisson distribution: modeling the number of events occurring within a fixed interval of time or space.

Exploring the fundamentals of Set Theory and Relations, and how these discrete structures underpin data categorization and recommendation systems in Machine Learning.