Announcements

First lecture is on 2nd March, 2021

Brief description of the course

The course offers exposure to basic mathematical and statistical principles and techniques of importance to bioengineers.

The topics to be covered include: linear algebraic equations; eigenvalues and eigenvectors; nonlinear algebraic equations; fixed-point iteration and optimization methods; linear and nonlinear least squares; first and second order ordinary differential equations; Euler, RK4, and predictor-corrector methods; discrete and continuous random variables; Markov processes; Gillespie algorithm; Monte Carlo methods; hypothesis testing; parametric and non-parametric statistical tests.

Prerequisites

Undergraduate engineering mathematics.

Grading policy

Assignments (3-4 through the semester): 15%, Quizzes (3-4 through the semester): 20%, Midterm Exam: 15%, Final Exam: 50%

Resources

1. Gilbert Strang, Differential equations and linear algebra, Wellesley-Cambridge,2015
2. Michael Heath, Scientific computing: an introductory survey, McGraw Hill,2005
3. Steven Strogatz, Nonlinear dynamics and chaos, Westview, 2015
4. Sheldon Ross, Introduction to probability models, Academic, 2014
5. Sheldon Ross, Introductory statistics, Academic, 2010