(Common for all B.Tech. programme except CS and IT)
3 hours lecture and 1 hour tutorial per week
Module I
Linear Algebra: Vector spaces- linear dependence and impedance, and their computation- Bases
and dimension- Subspaces- Inner product spaces- Gram-Schmidt orthogonalization process-
Linear transformations- Elementary properties of linear transformations- Matrix of a linear
transformation. (Proofs of theorems omitted)
Module II
Fourier Transforms: Fourier integral theorem (proof not required)- Fourier sine and cosine
integral representations- Fourier transforms- Fourier sine and cosine transforms- Properties of
Fourier transforms- Singularity functions and their Fourier transforms.
Module III
Probability Distributions: Random variables- Mean and variance of probability distributions-
Binominal and Poisson distributions- Poisson approximation to binominal distribution-
Hypergeometric and geometric distributions- Probability densities- Normal, uniform and gamma
Module IV
Theory of Inference: Population and samples- Sampling distributions of mean and variance-
Point and interval estimations- Confidence intervals for mean and variance- Tests of hypotheses-
Hypotheses concerning one mean, two mean, one variance and two variances- Test of goodness of

For Module I
K. B. Datta, Matrix and Linear Algebra for Engineers, Prentice-Hall of India, New Delhi, 2003.
For Module II
C R Wylie & L C Barrett, Advanced Engineering Mathematics (Sixth Edition), McGraw Hill.
For Module III
Richard A Johnson, Miller & Freund’s Probability and Statistics for Engineers, Pearson Education, 2000.
For Module IV
Richard A Johnson, Miller & Freund’s Probability and Statistics for Engineers, Pearson Education, 2000


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