🔢 MemoLearning Discrete Mathematics

Logic, sets, graph theory, and mathematical reasoning for computer science

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Curriculum Overview

14
Total Units
~170
Skills to Master
8
Core Units
6
Advanced Units
1

Logic and Propositional Calculus

Foundation of mathematical reasoning using logical statements and operations.

  • Propositions and truth values
  • Logical connectives (∧, ∨, ¬, →, ↔)
  • Truth tables and logical equivalence
  • Tautologies and contradictions
  • Logical arguments and validity
  • Rules of inference
  • Normal forms (CNF, DNF)
  • Boolean algebra
2

Predicate Logic and Quantifiers

Express complex mathematical statements using predicates and quantifiers.

  • Predicates and domains
  • Universal quantifier (∀)
  • Existential quantifier (∃)
  • Nested quantifiers
  • Negating quantified statements
  • Translating English to logic
  • Logical equivalences with quantifiers
  • Proof techniques with predicates
3

Proof Techniques

Master various methods for constructing mathematical proofs.

  • Direct proof
  • Proof by contraposition
  • Proof by contradiction
  • Proof by cases
  • Mathematical induction
  • Strong induction
  • Existence and uniqueness proofs
  • Common proof strategies
4

Set Theory

Understand collections of objects and operations on sets.

  • Set definitions and notation
  • Set operations (∪, ∩, -, ⊕)
  • Venn diagrams
  • Set identities and laws
  • Power sets
  • Cartesian products
  • Set cardinality
  • Russell's paradox
5

Functions and Relations

Study mappings between sets and relationships within sets.

  • Function definitions and notation
  • Injective, surjective, bijective functions
  • Inverse functions
  • Composition of functions
  • Relations and their properties
  • Equivalence relations
  • Partial orders
  • Hasse diagrams
6

Combinatorics

Count arrangements, selections, and combinations of objects.

  • Fundamental counting principles
  • Permutations and combinations
  • Binomial coefficients
  • Pascal's triangle
  • Inclusion-exclusion principle
  • Pigeonhole principle
  • Generating functions
  • Recurrence relations
7

Graph Theory Fundamentals

Analyze networks, connections, and graph structures.

  • Graph definitions and terminology
  • Degree sequences
  • Paths, cycles, and connectivity
  • Eulerian and Hamiltonian paths
  • Trees and spanning trees
  • Bipartite graphs
  • Graph coloring
  • Planar graphs
8

Number Theory

Explore properties of integers and divisibility.

  • Divisibility and modular arithmetic
  • Prime numbers and factorization
  • Greatest common divisor (GCD)
  • Euclidean algorithm
  • Congruences and their properties
  • Chinese remainder theorem
  • Fermat's little theorem
  • Cryptographic applications
9

Sequences and Series

Study ordered lists and their sums in discrete settings.

  • Arithmetic sequences
  • Geometric sequences
  • Fibonacci sequence
  • Recurrence relations
  • Solving linear recurrences
  • Generating functions
  • Summation formulas
  • Mathematical induction for sequences
10

Boolean Algebra and Logic Circuits

Apply Boolean logic to digital circuit design.

  • Boolean functions and expressions
  • Logic gates (AND, OR, NOT, XOR)
  • Truth tables for circuits
  • Minimization techniques
  • Karnaugh maps
  • Quine-McCluskey method
  • Circuit optimization
  • Applications in computer design
11

Advanced Graph Theory

Explore advanced concepts in graph theory and algorithms.

  • Graph algorithms (DFS, BFS)
  • Shortest path algorithms
  • Minimum spanning trees
  • Network flows
  • Graph isomorphism
  • Vertex and edge connectivity
  • Matching theory
  • Ramsey theory
12

Probability and Counting

Connect combinatorics with basic probability theory.

  • Sample spaces and events
  • Classical probability
  • Conditional probability
  • Independence
  • Bayes' theorem
  • Random variables
  • Expected value
  • Probability distributions
13

Formal Languages and Automata

Introduction to computational models and formal language theory.

  • Alphabets, strings, and languages
  • Regular expressions
  • Finite automata
  • Context-free grammars
  • Parse trees
  • Pushdown automata
  • Turing machines
  • Chomsky hierarchy
14

Applications in Computer Science

Apply discrete mathematics concepts to real computer science problems.

  • Algorithm analysis
  • Data structures
  • Database theory
  • Cryptography
  • Error-correcting codes
  • Compiler design
  • Software verification
  • Complexity theory

Unit 1: Logic and Propositional Calculus

Build the foundation of mathematical reasoning using logical statements and operations.

Propositions and Truth Values

Understand declarative statements that are either true or false, form the basis of logical reasoning.

Logical Connectives

Master AND (∧), OR (∨), NOT (¬), implication (→), and biconditional (↔) operators.

Truth Tables

Systematically evaluate compound propositions and determine logical equivalences.

Tautologies and Contradictions

Identify statements that are always true (tautologies) or always false (contradictions).

Logical Arguments

Analyze argument structure, distinguish between valid and invalid reasoning patterns.

Rules of Inference

Apply modus ponens, modus tollens, and other logical rules to construct valid arguments.

Normal Forms

Convert logical expressions to conjunctive normal form (CNF) and disjunctive normal form (DNF).

Boolean Algebra

Work with Boolean variables and operations, foundation for digital circuit design.

Unit 2: Predicate Logic and Quantifiers

Express complex mathematical statements using predicates and quantifiers.

Predicates and Domains

Understand functions that return true/false values and their domains of discourse.

Universal Quantifier (∀)

Express statements about all elements in a domain using "for all" quantification.

Existential Quantifier (∃)

Express statements about the existence of elements using "there exists" quantification.

Nested Quantifiers

Handle multiple quantifiers in a single statement, understand order importance.

Negating Quantified Statements

Apply De Morgan's laws to quantifiers, convert between ∀ and ∃ in negations.

Translating English to Logic

Convert natural language statements into precise logical notation using predicates.

Logical Equivalences

Identify equivalent statements involving quantifiers and apply transformation rules.

Proof with Predicates

Construct proofs using universal instantiation, existential generalization, and other rules.

Unit 3: Proof Techniques

Master various methods for constructing rigorous mathematical proofs.

Direct Proof

Prove implications P → Q by assuming P and showing Q follows logically.

Proof by Contraposition

Prove P → Q by proving the contrapositive ¬Q → ¬P instead.

Proof by Contradiction

Assume the negation of what you want to prove and derive a contradiction.

Proof by Cases

Break the problem into exhaustive cases and prove each case separately.

Mathematical Induction

Prove statements about natural numbers using base case and inductive step.

Strong Induction

Use all previous cases in the inductive hypothesis, not just the immediate predecessor.

Existence and Uniqueness

Prove that objects with certain properties exist and are unique.

Proof Strategies

Develop intuition for choosing appropriate proof techniques for different problems.

Unit 4: Set Theory

Understand collections of objects and fundamental operations on sets.

Set Definitions

Learn set notation, roster and set-builder notation, empty set, and universal set.

Set Operations

Master union (∪), intersection (∩), difference (-), and symmetric difference (⊕).

Venn Diagrams

Visualize set relationships and operations using overlapping circles and regions.

Set Identities

Apply commutative, associative, distributive, and De Morgan's laws for sets.

Power Sets

Understand the set of all subsets of a given set and its cardinality properties.

Cartesian Products

Form ordered pairs and n-tuples from multiple sets, basis for relations.