discretemath.org
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Discretemath.org is an educational resource focused on discrete mathematics. It provides tutorials, notes, and practice problems on core topics such as logic, set theory, combinatorics, graph theory, and algorithms to help learners master discrete math concepts.
More resources on Discrete Mathematics
Discrete Mathematics and Its Applications
Standard undergraduate discrete mathematics textbook covering logic, proof, sets, functions, algorithms, number theory, counting, relations, graphs and trees, with thousands of exercises. Its relations chapter develops equivalence relations, partial orders, closures and matrix representations to a level suited to computer science study.
Art of Problem Solving Discrete Math
The Art of Problem Solving wiki's entry point for discrete mathematics, defining the field and branching into combinatorics, graph theory, set theory, number theory, and abstract algebra. Written for competition students, with links into AoPS problem archives.
Discrete Mathematics: An Open Introduction
Learn the fundamentals of discrete mathematics! Explore logic, sets, functions, and more with this open-source introductory website.
Mathematics for Computer Science (MIT 6.042J)
Discrete mathematics for computer science: logic and proof methods, induction, sets and relations, graph theory and coloring, number theory and congruences, asymptotic notation, counting, recurrences and discrete probability. This gallery holds 25 lecture videos; the course also offers problem sets and exams with solutions.
Discrete Mathematics: An Open Introduction
A free, open-licensed undergraduate textbook covering counting and combinatorics, sequences and recurrence relations, symbolic logic, proof techniques including induction, and graph theory, with many worked exercises. Readers build the discrete foundations needed for computer science and upper-level mathematics.
Algorithmic Aspects of Machine Learning (MIT 18.409)
Designing machine learning algorithms with provable guarantees: nonnegative matrix factorization, topic models, tensor decomposition, sparse recovery, dictionary learning and learning mixture models. Moitra's detailed lecture notes, which read as a complete monograph, and problem sets teach rigorous analysis of learning algorithms.