**Why do I need to learn about discrete mathematics?**

Discrete mathematics is a fundamental tool for understanding many theories and techniques behind artificial intelligence, machine learning, deep learning, data mining, security, digital imagine processing and natural language processing.

The problem-solving techniques and computation thinking introduced in discrete mathematics are necessary for creating complicated software too.

**What can I do after finishing learning discrete mathematics?**

You will be prepared to learn modern theories and techniques to create modern security, machine learning, data mining, image processing or natural language processing software.

**What should I do now?**

Please read

– this Kenneth H. Rosen (2012). Discrete Mathematics and Its Applications. McGraw-Hill book and

– this Alfred V. Aho and Jeffrey D. Ullman (1994). Foundations of Computer Science book (free online version).

Alternatively, please watch this MIT 6.042J – Mathematics for Computer Science, Fall 2010 course (Textbook).

**Terminology Review:**

- Statement: An assertion that is either true or false.
- Mathematical Statements.
- Mathematical Proof: A convincing argument about the accuracy of a statement.
- If p, then q. p is hypothesis. q is conclusion.
- Proposition: A true statement.
- Theorem: An important proposition.
- Lemmas: Supporting propositions.
- Logic: A language for reasoning that contains a collection of rules that we use when doing logical reasoning.
- Propositional Logic: A logic about truth and falsity of statements.
- Logic Connectives: Not (Negation), And (Conjunction), Or (Disjunction), If then (Implication), If and only if (Equivalence).
- Truth Table.
- Contrapositive of Proposition: The contrapositive of p
**⇒**q is the proposition ¬q**⇒**¬p. - Modus Ponens: If both P
**⇒**Q and P hold, then Q can be concluded.

- Predicate: A property of some objects or a relationship among objects represented by the variables.
- Quantifier: Tells how many objects have a certain property.
- Mathematical Induction: Base Case, Inductive Case.
- Recursion: A Base, An Recursive Step.
- Sum Example: Annuity.
- Set.
- Subset.
- Set Operations: A ∪ B, A ∩ B, A ⊂ U: A’ = {x : x ∈ U and x ∉ A}, A \ B = A ∩ B’ = {x : x ∈ A and x ∉ B}.
- Cartesian Product: A × B = {(a; b) : a ∈ A and b ∈ B};
- A
*binary relation*(or just relation) from X to Y is a subset R ⊆ X × Y. To*describe*the relation R, we may list the collection of all ordered pairs (x, y) such that x is related to y by R. - A
*mapping*or*function*f ⊂ A × B from a set A to a set B to be the*special type*of relation in which for each element a ∈ A there is a*unique*element b ∈ B such that (a, b) ∈ f. - Equivalence Relation.
- Equivalence Class.
- Partition.
- A
*state machine*is a binary relation on a set, the elements of the set are called states, the relation is called the transition relation, and an arrow in the graph of the transition relation is called a transition. - Greatest Common Divisor.
- Division Algorithm.
- Prime Numbers.
- The Fundamental Theorem of Arithmetic: Let n be an integer such that n > 1. Then n can
*be factored*as a product of prime numbers. n = p₁p₂ ∙ ∙ ∙ pₖ - Congruence: a is congruent to b modulo n if n | (a – b), written a ≡ b (mod n).
- Fermat’s Little Theorem.
- Stirling’s Approximation.
- Probability.
- Example: The Monty Hall Problem.
- The Four Step Method: (1) Find the Sample Space (Set of possible outcomes), (2) Define Events of Interest (Subset of the sample space), (3) Determine Outcome Probabilities, (4) Compute Event Probabilities.
- A
*tree diagram*is a graphical tool that can help us work through the four step approach when the number of outcomes is not too large or the problem is nicely structured. - Example: The Strange Dice.
- Conditional Probability: P(A|B) = P (A ∩ B) / P(B).
- A conditional probability P(B|A) is called a
*posteriori*if event B*precedes*event A in time. - Example: Medical Testing.
- Independence: P(B|A) = P(B) or P(A∩B) = P(A) · P(B).
- Mutual Independence: The probability of each event is the same no matter which of the other events has occurred.
- Pairwise Independence: Any two events are independent.
- Example: The Birthday Problem.
- The
*birthday paradox*refers to the counterintuitive fact that only 23 people are needed for that probability to exceed 50%, for 70 people: P = 99.9%. - Bernoulli Random Variable (Indicator Random Variable): f: Ω ↦ {1, 0}.
- Binomial Random Variable: A number of successes in an experiment consisting of n trails. P (X = x) = [(n!)/((x!) · (n-x)!))]pˣ(1 − p)ⁿ − ˣ
- Expectation (Average, Mean). E = Sum(R(w) · P(w)) = Sum(x · P(X = x)).
- Median P(R < x) ≤ 1/2 and P(R>x) < 1/2.
- Example: Splitting the Pot.
- Mean Time to Failure: If a system independently fails at each time step with probability p, then the expected number of steps up to the first failure is 1/p.
- Linearity of Expectation.
- Example: The Hat Check Problem.
- Example: Benchmark: E(Z/R) = 1.2 does NOT mean that E(Z) = 1.2E(R).
- Variance: var(X) = E[(X−E[X])²].
*Kurtosis: E[(X−E[X])⁴].*- Markov’s Theorem: P(R ≥ x) ≤ E(R)/x (R > 0, x > 0).

- Chebyshev’s Theorem: P(|R – E(R)| ≥ x) ≤ var(R)/x². Boundary of the probability of deviation from the mean.

- The Chernoff Bound: P(T ≥ c·E(T)) ≤ e−ᶻ·ᴱ⁽ᵀ⁾, where z = clnc − c + 1, T = Sum(Tᵢ), 0 ≤ Tᵢ ≤ 1.

After finishing learning about discrete mathematics please click Topic 21 – Introduction to Computation and Programming using Python to continue.