EC388 Matrix Theory and Stochastic Process

Course Name: 

EC388 Matrix Theory and Stochastic Process


B.Tech (ECE)


Programme Specific Electives (PSE)

Credits (L-T-P): 

(3-1-0) 4


System of Equations - Homogenous equations, basic solutions, Echelon matrices, Linear independence, Rank, Inverse, Similarity, Eigen value analysis and Diagonalization, Vector Spaces: Linear Transformations, Subspaces, Linear Independence, Basis, Change of Coordinates, Orthogonal Transformations and applications. Probability - Probability space and definitions, Joint and Conditional probability, Bayes theorem. Random Variable - Definition, discrete and continuous, probability distribution and density, mass functions, Joint and conditional distributions Expectation, Moments and moment generating functions, Inequalities, limit theorems, random vectors, vectorized moments, mean and covariance, Random processes.


G. Strang, Linear Algebra and its applications, Thomson Learning, 2003.
Defranza and Gagliardi, Introduction to Linear Algebra with applications, Tata McGraw Hill, 2012
S. Lipschutz, Schaum's outline series of Linear Algebra, Tata Mc Graw Hill, 2012
H. Stark and JW Woods, Probability and Random processes with applications to signal processing, Pearson Ed, 2002
Peebles, Probability Random Variables and Random Signal Principles, McGraw Hill, 2002

Contact us

Dr. T. Laxminidhi,  Professor and Head, 
Department of E&C, NITK, Surathkal
P. O. Srinivasnagar,
Mangalore - 575 025 Karnataka, India.

  • Hot line: +91-0824-2473046

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