# Advanced Computational Techniques Semester 7 (BE Fourth Year) BE Civil Engineering University of Mumbai Topics and Syllabus

University of Mumbai Syllabus For Semester 7 (BE Fourth Year) Advanced Computational Techniques: Knowing the Syllabus is very important for the students of Semester 7 (BE Fourth Year). Shaalaa has also provided a list of topics that every student needs to understand.

The University of Mumbai Semester 7 (BE Fourth Year) Advanced Computational Techniques syllabus for the academic year 2022-2023 is based on the Board's guidelines. Students should read the Semester 7 (BE Fourth Year) Advanced Computational Techniques Syllabus to learn about the subject's subjects and subtopics.

Students will discover the unit names, chapters under each unit, and subtopics under each chapter in the University of Mumbai Semester 7 (BE Fourth Year) Advanced Computational Techniques Syllabus pdf 2022-2023. They will also receive a complete practical syllabus for Semester 7 (BE Fourth Year) Advanced Computational Techniques in addition to this.

CBCGS [2019 - current]
CBGS [2015 - 2018]
Old [2000 - 2014]

## University of Mumbai Semester 7 (BE Fourth Year) Advanced Computational Techniques Revised Syllabus

University of Mumbai Semester 7 (BE Fourth Year) Advanced Computational Techniques and their Unit wise marks distribution

## Syllabus

100 Review of Basic Statistics and Probability
• Probability Distributions, Theoretical: binomial, poisson, normal, exponential, hypergeometric, uniform
200 Sampling and Sampling Distributions
• Probability and non-probability samples, sampling and non-sampling errors.
• Sample size, sampling distributions: t, F and ƛ ²distributions.
300 Hypothesis Testing
• Type I and II error, testing of mean, proportion, tests for equality of mean and variances of two populations, confidence interval, ƛ ² test for goodness of fit, ANOVA (one way classification),Non parametric tests: sign test, U test
400 Correlation and Regression
• Karl Pearson's and Rank Correlation coefficient, simple linear regression least squares method.
500 Management Decision Making
• System approach, decision making under uncertainty and risk: decision tables and decision tree.
600 Linear Programming
• Graphical solution, simplex method, dual, sensitivity analysis, transportation and assignment problems
700 Introduction to Genetic Algorithms