Current Courses at TED University

Undergraduate

EE 304 – Probability and Random Variables

A third-year undergraduate core course covering probability models, discrete and continuous random variables, joint and conditional distributions, correlation, covariance, and expectation.

EE 311 – Signals and Systems

A third-year undergraduate core course covering continuous-time and discrete-time signals, linear systems, Fourier analysis, and sampling theory.

EE 413 – Communication Systems II

A senior-level undergraduate course covering digital communication systems, sampling, quantization, digital modulation, receiver design, noise effects, and introductory information theory concepts.

EE 491 / EE 492 – Senior Design Project I & II

A two-semester capstone sequence focused on the design and implementation of an electrical and electronics engineering system, emphasizing engineering analysis, technical reporting, and teamwork.

Graduate

EE 512 – Optimization for Communication Networks

Graduate-level course covering optimization techniques for communication networks, including linear, integer, mixed-integer programming, network flows, and wireless network applications.

Past Courses at TED University

Undergraduate

EE 205 – Software Tools for Electrical Engineering

A compulsory undergraduate course introducing MATLAB-based software tools and computational methods for electrical engineering applications.

EE 312 – Communication Systems I

A compulsory undergraduate course covering communication systems, modulation techniques, and noise analysis.

EE 462 – Power System Analysis

An undergraduate elective covering power system modeling, load flow, fault analysis, smart grids, and renewables.

Course materials, including lecture notes and supplementary resources, are available through the TEDU LMS.

Teaching Philosophy

My teaching philosophy integrates theory, practice, and active learning to foster independent thinking and lifelong learning. I emphasize:

• Interactive classroom discussions that promote collaborative problem-solving
• Two-stage collaborative quizzes combining group work with individual assessment
• Use of modern computational tools (e.g., MATLAB, tablets) for live problem-solving and visualization
• Connecting theoretical concepts to real-world engineering applications
• Preparing students for both industry and research careers

My goal is to create an engaging and supportive learning environment that helps students develop strong analytical and problem-solving skills.