technifyed

IIT Roorkee via NPTEL

Advanced Engineering Mathematics

Overview

This course is a basic course offered to UG/PG students of Engineering/Science background. It contains Analytic Functions, applications to the problems of potential flow, Harmonic functions, Harmonic conjugates, Milne’s method, Complex integration, sequences and series, uniform convergence, power series, Hadamard’s formula for the radius of convergence, Taylor and Laurent series, zeros and poles of a function, meromorphic function, the residue at a singularity, Residue theorem, the argument principle and Rouche’s theorem, contour integration and its applications to evaluation of a real integral, integration through a branch cut, conformal mapping, application to potential theory, review of unilateral and bilateral Z-transforms and their properties, application of calculus of residues for the inversion formula of Z- transforms and Laplace transforms, review of Fourier integrals and Fourier transforms, Finite Fourier transforms, discrete Fourier transforms and applications, basic concepts of probability, Bayes theorem, probability networks, discrete and continuous probability distribution, joint distribution, correlation coefficient, applications to problems of reliability, queueing theory, service time for a customer in a facility and life testing, testing of hypotheses. This course has tremendous applications in diverse fields of Engineering and Sciences such as Signal processing, Potential theory, Bending of beams etc.

INTENDED AUDIENCE : UG and PG students of technical institutions/ universities/colleges.

Syllabus 12

  1. Week 1
    • Analytic Function
    • Cauchy-Riemann Equations
    • Harmonic Functions, Harmonic Conjugates and Milne's Method
    • Applications to the Problems of Potential Flow-I
    • Applications to the Problems of Potential Flow-II
  2. Week 2
    • Complex Integration
    • Cauchy's Theorem-I
    • Cauchy's Theorem-II
    • Cauchy's Integral Formula for the Derivatives of Analytic Function
    • Morera's Theorem, Liouville's Theorem and Fundamental Theorem of Algebra
  3. Week 3
    • Winding Number and Maximum Modulus Principle
    • Sequences and Series
    • Uniform Convergence of Series
    • Power Series
    • Taylor Series
  4. Week 4
    • Laurent Series
    • Zeros and Singularities of an Analytic Function
    • Residue at a Singularity
    • Residue Theorem
    • Meromorphic Functions
  5. Week 5
    • Evaluation of real integrals using residues-I
    • Evaluation of real integrals using residues-II
    • Evaluation of real integrals using residues-III
    • Evaluation of real integrals using residues-IV
    • Evaluation of real integrals using residues-V
  6. Week 6
    • Bilinear Transformations
    • Cross Ratio
    • Conformal Mapping-I
    • Conformal Mapping-II
    • Conformal mapping from half plane to disk and half plane to half plane-I
  7. Week 7
    • Conformal mapping from disk to disk and angular region to disk
    • Application of Conformal Mapping to Potential Theory
    • Review of Z-transforms-I
    • Review of Z-transforms-II
    • Review of Z-transforms-III
  8. Week 8
    • Review of Bilateral Z-transforms
    • Finite Fourier Transforms
    • Fourier Integral and Fourier Transforms
    • Fourier Series
    • Discrete Fourier Transforms-I
  9. Week 9
    • Discrete Fourier Transforms-II
    • Basic Concepts of Probability
    • Conditional Probability
    • Bayes Theorem and Probability Networks
    • Discrete Probability Distribution
  10. Week 10
    • Binomial Distribution
    • Negative Binomial Distribution and Poisson Distribution
    • Continuous Probability Distribution
    • Poisson Process
    • Exponential Distribution
  11. Week 11
    • Normal Distribution
    • Joint Probability Distribution-I
    • Joint Probability Distribution-II
    • Joint Probability Distribution-III
    • Correlation and Regression-I
  12. Week 12
    • Correlation and Regression-II
    • Testing of Hypotheses-I
    • Testing of Hypotheses-II
    • Testing of Hypotheses-III
    • Application to Queuing Theory and Reliability Theory

Advantages and disadvantages

Advantages

  • Taught by IIT and IISc professors, and it follows the Indian university syllabus closely.
  • All videos and assignments are free on NPTEL and SWAYAM.
  • The certificate is recognised by many Indian universities for credit transfer and by GATE aspirants.
  • Great for GATE and semester exam preparation.
  • From Indian Institute of Technology Roorkee, a well-regarded name.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (12 parts) you can see before you start.

Disadvantages

  • The certificate needs a proctored exam at a centre, which has a fee.
  • Recorded classroom lectures: thorough, but slower than made-for-online courses.
  • New runs start on fixed dates (January and July).
  • Learning is free, but the certificate costs money.

Some points apply to every course of this kind; see how we rank.

Free to learn

  • Free: Every video and assignment is free on NPTEL and SWAYAM. Enrol when the next run opens.
  • Certificate: Optional. It needs a proctored exam at a centre, which has a fee.

Taught by

  • Prof. P. N. AgarwalIIT Roorkee

Indian Institute of Technology Roorkee is in Tier 2: excellent universities and the companies that build the technology of our institution ranking (90/100).

Similar courses

Compare these

Harvard University · edX

Fundamentals of TinyML

4.567 ratings

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

Free to audit5 weeks, 2 - 4 hours per week

Harvard University · edX

Introduction to Data Science with Python

4.3174 ratings

Learn the concepts and techniques that make up the foundation of data science and machine learning.

Free to audit8 weeks, 3 - 5 hours per week

Harvard University · edX

CS50's Introduction to Computer Science

An introduction to the intellectual enterprises of computer science and the art of programming.

Free to audit12 weeks, 5 - 14 hours per week

Harvard University · edX

Data Science: Building Machine Learning Models

4.4133 ratings

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

Free to audit8 weeks, 2 - 3 hours per week

Harvard University · edX

CS50's Introduction to Programming with Python

An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.

Free to audit10 weeks, 3 - 6 hours per week

Harvard University · edX

Data Science: R Basics

4.4273 ratings

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

Free to audit8 weeks, 2 - 3 hours per week

Enter your email and the official page opens. Phone is optional.