Course Title: 
Mathematics for Computer Science
Course Code: 
Year Taught: 
Postgraduate (PG)
School of Engineering

'Mathematics for Computer Science' is a course offered in the first semester of M. Tech., in Computer Science and Engineering at School of Engineering, Amrita Vishwa Vidyapeetham.

Linear Algebra for Computer Science

Vector – Vector operations – Advanced Vector operations – Slicing and Dicing – Linear transformations and Matrices – Principle of Mathematical Induction – Special Matrices – Vector Spaces – Span, Linear Independence, and Bases - Orthogonal Vectors and Spaces – Linear Least Squares – Eigenvalues, Eigenvectors, and Diagonalization – Applications in Computer Science.


  • Ernest Davis, “Linear Algebra and Probability for Computer Science Applications”,CRC Press, 2012.
  • Gilbert Strang, “Introduction to Linear Algebra”, Fourth Edition, Wellelsley- Cambridge Press, 2009.
  • Howard Anton and Chris Rorrers,”Elementary Linear Algebra”, Tenth Edition, 2010 John Wiley & Sons, Inc.

Probability and Statistics for Computer Science

Introduction to Statistics and Probability – Probability and Conditioning – Conditional Probability – Baye’s rule – Random variables – Expectation and Variance – Covariance – Discrete and Continuous Distributions – Central Limit Theorem – Statistics and Parameter estimation – Confidence intervals and Hypothesis testing.


  1. David Forsyth, “Probability and Statistics for Computer Science”, Springer international publishing, 2018
  2. Ernest Davis, “Linear Algebra and Probability for Computer Science Applications”,CRC Press, 2012.
  3. Douglas C. Montgomery and George C. Runger, “Applied Statistics and Probability for Engineers”, Third Edition, John Wiley & Sons Inc., 2003.
  4. Ronald E. Walpole, Raymond H Myres, Sharon.L.Myres and Kying Ye, “Probability and Statistics for Engineers and Scientists”, Seventh Edition, Pearson Education, 2002.
  5. A. Papoulis and Unnikrishna Pillai, “Probability, Random Variables and Stochastic Processes”, Fourth Edition, McGraw Hill, 2002.

Course Outcome

  Course Outcome Bloom’s Taxonomy Level
CO 1 Understand the key techniques and theory behind the type of random variable and distribution L2
CO 2 Use effectively the various algorithms for applications involving probability and statistics in computing (data analytics) L3
CO 3 Evaluate and Perform hypothesis testing and to conclude L4, L5
CO 4 Design and build solutions for a real world problem by applying relevant distributions L4, L5