📄️ Sets & Relations
Exploring the fundamentals of Set Theory and Relations, and how these discrete structures underpin data categorization and recommendation systems in Machine Learning.
📄️ Logic
Exploring propositional logic, logical operators, and Boolean algebra as the basis for decision-making algorithms and binary classification.
📄️ Combinatorics
Mastering permutations, combinations, and counting principles essential for understanding probability, feature engineering, and model complexity.
📄️ Graph Theory
Exploring the fundamentals of graph theory, including nodes, edges, adjacency matrices, and their applications in neural networks and Knowledge Graphs.