CS6015: Linear Algebra and Random Processes

Course information

Prashanth Wednesday 2:30pm to 3:30pm BSB 314
Abhishek Monday 2:30pm to 3:30pm DON lab
Hardi Tuesday 2:30pm to 3:30pm DON lab
Ditty Wednesday 2:30pm to 3:30pm AIDB lab
Jom Thursday 2:30pm to 3:30pm DON lab
Purvi Friday 2:30pm to 3:30pm DON lab

Course Content

Linear Algebra


Matrix Multiplication, Transposes, Inverses, Gaussian Elimination, factorization A=LU, rank

Vector spaces

Column and row spaces, Solving Ax=0 and Ax=b, Independence, basis, dimension, linear transformations


Orthogonal vectors and subspaces, projection and least squares, Gram-Schmidt orthogonalization


Determinant formula, cofactors, inverses and volume

Eigenvalues and Eigenvectors

Characteristic polynomial, Diagonalization, Hermitian and Unitary matrices, Spectral theorem, Change of basis

Positive definite matrices and singular value decomposition

Random processes


Events, probability, conditional probability, independence, product spaces

Random Variables

Distributions, law of averages, discrete and continuous r.v.s, random vectors, Monte Carlo simulation

Discrete Random Variables

Probability mass functions, independence, expectation, conditional expectation, sums of r.v.s

Continuous Random Variables

Probability density functions, independence, expectation, conditional expectation, functions of r.v.s, sum of r.v.s, multivariate normal distribution, sampling from a distribution

Convergence of Random Variables

Modes of convergence, Borel-Cantelli lemmas, laws of large numbers, central limit theorem, tail inequalities

* Advanced topics (if time permits)

Markov chains, minimum mean squared error estimation


  • Mid-term (Linear Algebra concepts): 30%

  • Final exam (Probability concepts): 30%

  • Quizzes: 20% (Best 5 out of 8)

  • Programming Assignments: 20%

Target Audience

Masters (M.Tech/M.S.) and Ph.D. students

Important Dates

Problem Sets Quizzes Tutorials Mid-Sem End-Sem
Aug 7 Aug 16 Aug 11 When: 10am to 12pm, Sep 24      Where: CS15 Nov 15
Aug 18 Aug 28 Sep 1
Aug 25 28 Sep 5 Sep 20
Sep 11 Sep 18 Oct 13
Sep 28 Oct 6 Oct 23
Oct 10 Oct 17 Nov 10
Oct 20 Oct 27
Oct 27 Nov 3


  • Linear algebra and applications by Gilbert Strang

  • Probability and random processes by Geoffrey Grimmett and David Stirzaker

Additional references:

  • Introduction to probability models by Sheldon Ross

  • Linear Algebra: Pure and Applied by Edgar Goodaire


Lecture number Topics Covered Section reference
(Strang's book)
Lecture 1 Course Organization
Motivation for studying linear algebra
Lecture 2 Geometry of linear equations - row picture, col picture
Vector space, subspace - definition, examples Linear combinations, linear independence
Lecture 3 Transpose - properties
Gaussian elimination
Lecture 4 Computational cost of elimination
Matrix multiplication - four view
Gauss-Jordan method
Lecture 5 Factorization A=LU and A=LDU
Row exchanges, permutation matrices
Uniqueness of LU/LDU factorization for invertible matrices
Lecture 6 Column and null spaces
Null space computation by solving Ax=0
Pivot and free variables, special solutions
row reduced echelon form
2.1, 2.2
Lecture 7 Dimension = number of vectors in any basis
Rank, row-rank = col-rank
rank + nullity = number of columns
2.3, 2.4
Lecture 8 Orthogonal vectors and subspaces
Row space orthogonal to null space
Quiz 1
Lecture 9 Projection onto a line
Projection onto a subspace
Lecture 10 Least squares data fitting
Orthonormal vectors
Lecture 11 Four fundamental subspaces (again)
Least squares data fitting
2.4, 3.3
Lecture 12 Orthogonal bases
Gram Schmidt algorithm
Factorization A=QR
Quiz 2
Lecture 13 Linear transformations:
definition, matrix representation
Lecture 14 Composition of linear transformations
Change of basis
Change of basis: Section 46 of Halmos's text
Lecture 15 Similarity of transformations Section 47 of Halmos's text
Quiz 3
Lecture 16 Determinants: Properties, Formula 4.2, 4.3
Lecture 17 Determinants: Cofactors, Applications in graphs 4.3, 4.4
Graph applications: Section 3.3 of Goodaire's text
Lecture 18 Eigenvalues and Eigenvectors 5.1
Lecture 19 Similarity and diagonalization 5.2
Lecture 20* Complex vectors and matrices 5.5
Lecture 21* Unitary diagonalization 5.5
Lecture 22* Real symmetric matrices
Spectral theorem
Lecture 23* Singular value decomposition 6.3
* Topics covered subject to change based on class feedback