ITM 153

Discrete Structure

TU BITM / BIM · Semester 2 · BITM curriculum and programme regulation

Requirement
required
Credits
3
Past papers
2 papers

Syllabus

What this course covers and how the teaching time is divided.

Discrete Structure Syllabus

Official TU PDF

Tribhuvan University

Faculty of Management

Office of the Dean

Bachelor of Information Technology Management (BITM / BIM) (BITM / BIM)

Course Title: Discrete Structure

Course Code: ITM 153

Semester: Semester 2

Nature of Course: required

Full Marks: 100

Pass Marks: 50

Credit Hours: 3 Cr.

Curriculum: BITM curriculum and programme regulation

Course Description

:

This course introduces students to the concepts of mathematical structures that are fundamentally countable. It provides the toolkit to handle discrete data, things that cannot be divided. Students learn to know about the mathematical background of the pure computing like logic, number theory, set theory, counting techniques, proof techniques, graphs and tree.

Course Objective

:

By the end of this course, students will be able to Design truth tables and logical connectives in business contracts Design Venn diagram to show market segmentation Design implicative business rules and identifying logical fallacies in marketing Use counting principles for product bundling Use binomial theorem in quality control and discrete probability in financial forecasting Use graph theories in modeling supply chain codes, delivery cost reduction, identifying critical paths in project timelines, analyzing influence within a market

Course Contents:

Lecture hours show the approximate classroom time allocated to each unit.

Unit 1. Logic and Proofs

10 hours
  • Propositional logic, Logical O perators (AND, OR, NOT, IMPLICATION with its variants, BICONDITIONAL), Laws of logical equivalences, Translating English sentences, Predicate and Quantifiers, Nested and Order in quantifiers, Translating English sentences using quantifiers, Rules of inferences for propositional logic , Valid arguments in propositional logic, Fallacies, Rules of inferences for quantified statements , Valid arguments in quantified statements, Methods of Proving Theorems (Direct Proof, Indirect Proof, Proof by Contradiction, Vacuous and Trivial Proof , Proof of Equivalence , Exhaustive Proof, Proof by Cases , Existence Proof, Uniqueness Proof, Counter
  • Example), Mistakes in Proof.
  • Learning Outcome: Use connectives like AND ( ), OR ( ), NOT ( ), and implication () to evaluate the truth value of complex statements (Apply). Describe the different rules of inferences for propositional logic and quantified statements (Understand).
  • Identify the techniques of proof (Remember).

Unit 2. Set Theory and Functions

2 hours
  • Sets, Ways to describe the sets, Venn diagram, Subset, Size of a set, Power set, Cartesian product, Set operations, Set identities, Computer representations of set, Functions, One to one function, Onto function, Bijection, Inverse functions, Composition of functions, Graph of functions, Floor functions, Ceiling functions, Sequence and Summations, Boolean matrices, Meet and Join operation on Boolean matrices.
  • Learning Outcome: Describe the sets and Venn diagram (Understand). Use the different representation techniques of set (Apply). Identify the types of functions (Remember).

Unit 3. Number Theory

5 hours
  • The division algorithm, Modular Arithmetic, Representation of Integers, Algorithms for Integer Operations, Primes, Greatest Common Divisors, Linear Congruences, The Chinese Remainder Theorem, Computer Arithmetic with Large Integers.
  • Learning Outcome: Explain the Chinese Remainder Theorem (Understand). Perform the modular arithmetic operations (Apply). Identify the prime (Remember).

Unit 4. Mathematical Induction and Recursion

4 hours
  • Introduction to Mathematical Induction, Proof by Mathematical Induction, Strong Induction, Well Ordering Property, Recursively Defined Functions and Sets, Structural Induction, Generalized Induction, Recursiv e Algorithms, Proving the Correctness of Recursive Algorithms.
  • Learning Outcome: Describe the steps of mathematical induction (Understand). Use it to proof the inequalities (Apply). Writing of the recursive algorithms (Remember).

Unit 5. Basics of Counting

4 hours
  • Sum Rule, Product Rule, Principle of Inclusion – Exclusion, The Pigeonhole Principle (Generalized as well) , Permutations and Combinations, Binomial Theorem, Pascal’s Identity and Triangle, Generalized Permutations and Combinations, Permutations with Repetitions, Combinations with Repetitions, Permutations with Indistinguishable Objects Learning Outcome : Explain the generalized Pigeonhole principle (Understand).
  • Generates permutations and combinations (Apply). What is Pascal’s triangl e?
  • (Remember).

Unit 6. Discrete Probability

4 hours
  • Introduction, Finite Probability, Probabilities of Complements and Unions of Events, Assigning Probabilities, Conditional Probability, Independence, Random Variable, The
  • Birthday Problem, Expected Value.
  • Learning Outcome: What is probability (Understand)? What are the uses of Conditional Probability (Apply)? What is random variable (Remember)?

Unit 7. Advanced Counting Techniques

5 hours
  • Recurrence Relations, Modeling with Recurrence Relations, Solving Linear Homogeneous Recurrence Relations with Constant Coefficients ( Without Proving the Theorem), the Degree of Two Case ( Two D istinct or Equal Characteristic R oots), the General Case ( the Degree may be Greater than Two, where the Characteristic E quation has Distinct Roots or Repeated Roots).
  • Learning Outcome: Describe the recurrence solution (Understand). Solve the recurrence relations (Apply). Why do we need to solve it (Remember)?

Unit 8. Relations

3 hours
  • Relation and its Properties, n – ary Relations, Representing Relations (using Matrix and Digraphs), Closure of Relations (Reflexive, Symmetric , Transitive ), Warshall’s Algorithm to Compute the Transitive Closure of a Relation, Equivalence Relation s, Equivalence Classes, Partial Ordering.
  • Learning Outcome : Describe the properties of relation (Understand). Represent the relations using matrix and directed graph (Apply). What is Partial Ordering (Remember)?

Unit 9. Graph Theory

8 hours
  • Graph Models, Types of Graphs ( Simple Graph, Multigraph, Pseduograph, Directed Graph, Null Graph , Bipartite Graph ), Graph Terminologies (Adjacent Vertices, Degree of a Vertex, Isolated Vertex, Pendant Vertex ), Handshaking Theorem, Representation of Graphs (Adjacency List, Adjacency Matrix, Incidence Matrix ), Graph Isomorphism, Graph Connectivity, Euler and Hamilton Path, Necessary and Sufficient Conditions for Euler and Hamilton Path and Circuits (Without Proof ), Shortest Path Al gorithm
  • (Dijkstra’s Algorithm), Planar Graph, Graph Coloring Learning Outcome : Explain the different types of graphs ( Understand). Know the theorem related to graph (Apply). What is the use of Dijkstra’s Algorithm (Remember)?

Unit 10. Trees

3 hours
  • Introduction to Trees, Rooted Tree, Terminologies of a Tree (Parent, Child, Sibling, Ancestors, Descendants, Leaf, Internal Nodes), M – ary Tree, Binary Search Tree, Decision Tree, Prefix Codes, Tree Traversal, Spanning Tree, Minimum Spanning Tree, Kruskal’s Algorithm.
  • Learning Outcome : Describe the terminologies of a tree ( Understand). Find the MST
  • (Apply). What is M – ary tree (Remember)?
  • Pedagogical Strategies
  • Lectures with demonstration
  • Hands-on lab sessions
  • Problem-based learning
  • Guest lectures from tech industry experts
  • Continuous assessment and feedback
  • Multimedia presentations to visualize concepts
  • Mini project
  • Mode of Delivery
  • Lecture sessions (Theory)
  • Demonstration
  • Laboratory work (Practical)
  • Mini project
  • Internal Assessment Methods and Types (40%)
  • Assessment Type Weightage Details Class participation & attendance 10% Contribution to discussions, engagement in class activities Quizzes/short tests 15% Periodic quizzes to assess comprehension
  • Practical/Project 20% Lab sessions and mini project
  • Mid-term examination 25% Written test Pre-board examination 30% Comprehensive written test covering all units External Assessment Methods and Types (60%) Out of the total 60% allocated for final assessment, 40% will be assigned to the written/board examination to evaluate students’ abilities in remembering, understanding, applying, analyzing, evaluating, and creating. The remaining 20% will be assigned to the final practical examination to assess hands-on programming skills and competency.
  • Mapping Course: Learning Outcomes and Program Learning
  • (CLO) Dimensions
  • Laboratory Works (16 hours) The laboratory work includes writing programs for implementing the concepts of logic gates, boolean matrix operations, floor and ceiling value of given positive as well as negative real numbers, set theory, number theory and graph theory using a programming language like C.

Suggested Readings:

Kenneth H. Rosen (2019), Discrete Mathematics and Its Applications , Eighth Edition, McGraw Hill Education. Susanna S. Epp (2020), Discrete Mathematics with Applications, Fifth Edition Course Learning Objective Knowledge (K) Skills (S) Competence (C) Total Learning 35% 40% 25%