📚 Discrete Mathematics for AI/ML – Full Roadmap
Discrete mathematics provides the foundation for modeling, reasoning, and decision-making in AI & ML. Sets, relations, and functions help structure data, define relationships, and map inputs to outputs.
Why Discrete Mathematics Matters in AI & ML
Discrete mathematics is critical in AI & ML because data is often structured in discrete formats. Concepts like sets, relations, and functions allow models to represent datasets, features, and labels efficiently. Understanding these principles helps in:
- Representing and manipulating datasets, features, and labels.
- Mapping inputs to outputs using functions.
- Defining relationships and similarity between data points.
- Designing algorithms with logical and combinatorial structures.
Core Topics in Chapter 1
- Sets: Definition, subsets, power set, universal set, and set operations (union, intersection, difference, complement).
- Relations: Reflexive, symmetric, transitive, equivalence relations, and their representation in AI/ML.
- Functions: One-to-one, onto, bijective functions; mapping inputs to outputs and feature transformations.
- ML Use Cases: Representing features, labels, datasets; mapping inputs to outputs; graph-based algorithms; equivalence classes for clustering.
- Python Examples: Implementing sets, set operations, and simple functions for data representation and transformations.
Chapters Roadmap
📌 Suggested Learning Flow
- Sets → Set operations → Relations → Functions → ML applications
- Implement simple Python examples for each concept
- Understand mappings and equivalence for feature representation and clustering
📚 Recommended Resources
- Book: Discrete Mathematics and Its Applications by Kenneth H. Rosen
- YouTube: Computer Science series on Discrete Math by MIT OpenCourseWare
- Python Libraries: sets, itertools for combinatorial operations
- Practice: Represent datasets, labels, and relationships using sets, relations, and functions in Python
Next Steps: Click on each chapter to explore detailed tutorials, examples, and Python exercises for AI & ML applications.