Engineering Mathematics -II (BSC-201)
Program: B.Tech Computer Science and Engineering (Artificial Intelligence and Machine Learning)
Category: Basic Science Course
Semester: 2
Credits: 5
L-T-P: 3- 2- 0
Description
Instructor

Dr. Ashish Kumar

Assistant Professor
Department Of Applied Sciences
Course Outcomes
  • To understand probability and random variables and various discrete and continuous probability distributions and their properties
  • Calculate probabilities, and derive the marginal and conditional distributions of bivariate random variables
  • Analyse statistical data using measures of central tendency, dispersion and location
  • Understand and discuss the issues surrounding sampling and significance
  • Develop analytical skills in structuring and interpreting the business problems statistically
Resources
Course Handout Lesson Plan Syllabus
Evaluation Scheme
MST 10
MST 10
Assignment 20
Attendance 10
Total Internal 50
Total External 100
Overall Total 150
Course Materials

Unit 1

Theory of Computation
Axioms of Probability
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Bernoulli trials
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Binomial distribution
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Conditional probability
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Discrete random variables
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Expectation, Variance and moments of discrete random variables
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Joint and Marginal distribution of discrete random variables
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Multinomial distribution
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Poisson distribution
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Probability mass function
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Probability spaces
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Unit 2

Theory of Computation
Chebyshev's Inequality
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Introduction to Continuous Random Variables
Bayes' theorem
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Bivariate distributions
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Conditional density
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Continuous random variables
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Distribution functions
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Expectation of continuous random variables
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Exponential distribution
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Gamma distribution
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Marginal density
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Normal distribution
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Probability density functions
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Variance of continuous random variables
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Unit 3

Correlaion & Regression
Central tendency
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Coefficient of regression
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Correlation coefficients
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Kurtosis and Coefficient of kurtosis
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Lines of regression
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Mean, median, and mode
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Measures of dispersion
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Quartile deviation
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Rank correlation methods
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Skewness and Karl Pearson's coefficient of skewness
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Unit 4

Statistics-I
Concept of Test Statistic
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Errors in Hypothesis Testing
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Introduction to Statistical Hypothesis
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Null and Alternative Hypotheses
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Paired t-Test
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t-Test for Difference of Means (Independent Samples)
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t-Test for Single Mean
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t-Test: Introduction and Conditions
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Z-Test for Difference of Means
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Z-Test for Single Mean
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Z-Test: Introduction and Conditions
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Unit 5

Statistics-II
Assumptions and Application of F-Test
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Assumptions and Limitations of Chi-Square Test
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Chi-Square Test for Goodness of Fit
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Chi-Square Test for Independence of Attributes
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F-Test for Equality of Two Variances
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Introduction to Chi-Square Test
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Introduction to F-Test
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PPT
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Question Bank
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