Available courses

23A35102 - Complex variables and Numerical Methods

Circuits and Signals: Complex Variables and Numerical Methods

Advanced electrical engineering operates heavily on the foundation of complex mathematics. In this course, we dive deeply into the properties of complex variables alongside numerical computation to tackle intricate electrical networks, power systems, and signal transformations. Join us to master the analytical frameworks used to analyze circuits and evaluate wave phenomena.

By the end of this course, you will be able to:
•    Analyze Complex Functions: Evaluate limits, continuity, and differentiation of complex variables while mastering analytic and harmonic functions critical for electromagnetic theory.
•    Evaluate Complex Systems: Apply Cauchy’s theorems and residue contour integration to solve complex integrals, while using numerical methods to resolve standard differential equations.
•    Forecast System Behavior: Utilize finite differences, Lagrange’s formulae, and curve fitting to model electrical component data and predict hardware performance.

Your Goal: To master the advanced analytical and computational frameworks vital for modern electrical and electronic engineering.

23A35103 - Numerical Methods & Transform Techniques

Modeling Mechanics: Numerical Methods & Transform Techniques

Precision computation is the engine behind modern mechanical design. In this course, we bridge theoretical mathematics with practical physical applications using numerical methods and advanced transform techniques. Join us to master the computational tools needed to analyze dynamic systems, process continuous signals, and solve mechanical engineering challenges.

By the end of this course, you will be able to:
•    Solve Dynamic Systems: Apply numerical iteration and approximation methods to solve the algebraic, transcendental, and differential equations that govern physical machinery.
•    Transform and Analyze: Utilize Laplace and Fourier transforms to process engineering signals, analyze vibrations, and resolve complex boundary value problems.
•    Map Experimental Data: Convert raw empirical data into predictive mathematical curves and equations using finite differences, interpolation, and least squares fitting.

Your Goal: To equip yourself with the computational toolkit essential for advanced mechanical analysis and design.

23A35106 - Numerical Methods & Laplace Transforms

Process Dynamics: Numerical Methods & Laplace Transforms

Understanding reaction kinetics and controlling plant operations requires robust mathematical frameworks. In this course, we explore how to translate complex chemical phenomena into solvable models using numerical simulations and transform methods. Join us to develop the mathematical proficiency needed to predict system behaviors, control chemical processes, and optimize production flows.

By the end of this course, you will be able to:
•    Compute Process Variables: Employ numerical techniques to solve the ordinary differential and transcendental equations frequently encountered in chemical reactor design.
•    Control Dynamic Systems: Leverage Laplace and inverse Laplace transforms to evaluate system models, handle elementary functions, and design stable process controls.
•    Model Experimental Reality: Transform laboratory data into reliable mathematical models using Newton’s interpolation formulae and exponential curve fitting.

Your Goal: To build the foundational mathematical skills required to optimize, model, and control real-world chemical processes.

Statistical Inference: Estimation and Hypothesis Testing

Statistical Inference: Estimation and Hypothesis Testing teaches how to draw conclusions about populations from sample data using confidence intervals and hypothesis tests.

You’ll learn large-sample Z-tests (means and proportions), small-sample t-tests and F-tests, and chi-square tests for categorical data through worked examples and practice.


Recommended background: basic probability, normal distribution, and fundamentals of sampling and estimation.