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109 changes: 109 additions & 0 deletions courses/MAIC201.md
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---
code: MAIC201
title: Discrete Mathematics and Statistical Methods

similar: [MAIR24]
needs_screening: true

specifics:
- branch: CS
semester: 3
credits: [3, 0, 0, 3]

- branch: IT
semester: 3
credits: [3, 0, 0, 3]

- branch: AI
semester: 3
credits: [3, 0, 0, 3]

prereq: []
kind: EPR
---

# Objectives

- To give the students deeper knowledge about Probability and Distributions.
- To study the topics of discrete mathematics with engineering applications.
- To apply graph theory based tools in solving practical problems.
- To impart knowledge about Statictical Methods.

# Content

## Unit 1

1. **Discrete Probability:**
- Basic definitions
- Engineering applications of probability
- Set theory
- Probability Multiplication principle
- Product of sums principle
- Cross product of sample spaces
- Theorem of Total probability
- Conditional Probability
- Mutual Exclusion and Independent Events
- Principle Of Inclusion and Exclusion
- Bayes' Rule

## Unit 2

1. **Discrete Random Variable & Distributions:**
- Random variables and their event spaces
- Probability Mass function
- Distribution function
- Mean & Variance of random variables
- Expected value of random variable & Computation of expectation for one variable
- Bernoulli Trial & Binomial distribution
- Poisson distribution
- Normal distribution
- Recurrence relation and their solutions

## Unit 3

1. **Graphs:**
- Basic Terminology
- Multigraphs and Weighted Graphs
- Paths and Circuits
- Shortest Paths in Weighted Graphs
- Eulerian Paths and Circuits
- Hamiltonian Paths and Circuits
- Planar Graphs

## Unit 4

1. **Relation and Logic:**
- Binary Relation and their properties
- Equivalence relations and partitions
- Partial ordering relations
- Linear ordering relations
- Chains
- Functions and Pigeonhole principle
- Propositions

## Unit 5

1. **Statistical Methods:**
- Large sample tests
- Procedure of testing hypothesis
- Small sample tests
- Student's T-test
- Chi-Square test
- Independence of attributes and goodness of fit

# Reference Books

- Joe L Mott, Kandel and Baker, Discrete Mathematics for Computer Scientists
- J. P. Tremblay and R Manohar Discrete Mathematical Structures with applications to Computer Science, Tata McGraw Hill (2001)
- Ronald E.Walpole, Raymond H. Myers, Sharon L. Myers, Keying Ye, Probability and Statistics for Engineers and Scientists, 9th Edition, 2012
- Kolman, Bubby Ross, Discret Mathematics Structures, PHI, 2001
- C.L. Liu,; Elements of Discrete Mathematics. 1986
- Gary Haggard, J. Schlipf, S. Whitesides, Discrete Mathematics for Computer Science, Cengage Learning; 2005


# Outcomes

- To use discrete and continuous probability distribution in practical problems.
- Use the relations, prepositions, and graph theory in practical engineering.
- To apply the different testing tools like Student's T-test, Shi-Square test, etc. to analyse the relevant real-life problems.
- Prove mathematical theorems using mathematical inductions.