Logic gates run on propositional logic. Hash maps are sets. Graph algorithms are just graph theory applied. This course connects the discrete math in your CS textbook to the code you write every day.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on discrete math and computer science fundamentals you can find — even without producing a single minute of custom video.
This course is built by people who ship production discrete systems for a living. It reflects how things actually work on real projects — not how the documentation describes them.
Every day has working code snippets you can paste into your editor and run right now. The emphasis is on understanding what each line does, not memorizing syntax.
Instead of shooting videos that go stale in six months, Precision AI Academy links to the definitive open-source implementations, official documentation, and the best conference talks on the topic.
Each day is designed to finish in about an hour of focused reading plus hands-on work. You can do the whole course over a week of lunch breaks. No calendar commitment, no live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
Propositional logic, truth tables, De Morgan's laws, and logical equivalences. The same rules that govern if-statements, SQL WHERE clauses, and digital circuits.
Set notation, union/intersection/complement, Cartesian products, relations, and functions as mathematical objects. The foundation of type theory and database joins.
Directed and undirected graphs, paths, cycles, trees, and connectivity. Directly maps to dependency graphs, social networks, routing algorithms, and AST traversal.
Counting arrangements, permutations, combinations, and basic probability. Directly applicable to algorithm analysis, hash collision probability, and randomized testing.
Direct proofs, proof by contradiction, and mathematical induction. How to reason rigorously about algorithm correctness and loop invariants.
Instead of shooting our own videos, Precision AI Academy links to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
Covers logic, set theory, graph theory, and proofs with worked examples for every major theorem.
Graph algorithms explained from the discrete math foundation — BFS, DFS, shortest paths, and spanning trees.
How to construct inductive proofs and how loop invariants map to the same reasoning pattern.
Counting, permutations, combinations, and pigeonhole principle applied to algorithm analysis.
The best way to understand any technology is to read the production-grade implementations that prove it works. These repositories implement patterns from every day of this course.
Python graph library used in Day 3 exercises. Source shows how classic graph algorithms (BFS, DFS, Dijkstra) are actually implemented.
Python computer algebra system. Useful for Day 1 logical equivalence checking and Day 4 combinatorics calculations.
Python implementations of every major algorithm from the CS canon. The graph section directly uses Day 3 concepts.
Clean Python implementations of data structures and algorithms. Useful for seeing how discrete math concepts become working code.
You never took a CS theory course. This course covers the discrete math that shows up in every algorithm textbook and system design interview.
Discrete math is the prerequisite for analysis of algorithms. This course teaches the parts that confuse most students — graphs, proofs, and induction.
ML papers, distributed systems papers, and cryptography papers all assume this background. This course gives you the notation literacy to follow the arguments.
The 2-day in-person Precision AI Academy bootcamp covers discrete math and computer science fundamentals hands-on. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
Reserve Your Seat