Digital Signal Processing and Laboratory
Mathematics for Electrical Engineers
Statistical Foundation for Electrical Engineers
Advanced Digital Signal Processing
Elements of Electrical Engineering
Signals and Systems
Linear and Digital Control Theory

Digital Signal Processing with Laboratory

This course deals with topics such as sampling, alias, frequency domain representation of signals, Discrete Fourier Transform (DFT), filter design, and conversion of filters from continuous to discrete domain. The course is offered along with a signal processing laboratory wherein real-life data is analyzed using Matlab/Octave. A course project is also given as part of this course.

Statistical Foundation for Electrical Engineers

This is a mathematics elective on introductory concepts in probability theory and its applications to electrical engineering, signal processing and machine learning. It covers Probability Models and Axioms, Random Variables, Expectation, variance, moments, moment generating function, Random Vectors, Conditional distributions, independence of random variables, Noise Modelling and Estimation etc. A course project is given as part of the project and the students are encouraged to use Matlab/Python to work out case studies.

Mathematics for Electrical Engineers

This course aims to introduce topics in mathematics important to electrical engineers - Linear Algebra and Fourier Analysis. These topics provides for the foundations in several electrical engineering subjects like electrical circuits, power systems, power electronics, discrete control systems among others, and allied electrical engineering subjects like signal processing and machine learning.

Linear Control Theory

This introductory course on control theory covers topics such as mathematical modeling of systems, dominant pole design of closed loop control, stability concepts along with frequency domain modelling.

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