Online Electrical Engineering Master's Program

The theory of linear algebra with application to state space analysis. Topics include Cauchy-Binet and Laplace determinant theorems, system of linear equations; linear transformations, basis and rank; Gaussian elimination; LU and congruent transformations; Gramm-Schmidt; eigenvalues, eigenvectors and similarity transformations; canonical forms; functions of matrices; singular value decomposition; generalized inverses; norm of a matrix; polynomial matrices; matrix differential equations; state space; controllability and observability.

This course presents tool, techniques, algorithms, and programming techniques using the Python programming language for data intensive applications and decision making. The course formally introduces techniques to: (i) gather,(ii) store, and (iii) process large volumes of data to make informed decisions. Such techniques find applicability in many engineering application areas, including communications systems, embedded systems, smart grids, robotics, Internet, and enterprise networks, or any network where information flows and alters decision making.

An introduction to classic and modern feedback control that does not presume an undergraduate background in control. Transfer function and state space modeling of linear dynamic systems, closed-loop response, root locus, proportional, integral, and derivative control, compensators, controllability, observability, pole placement, linear–quadratic cost controllers, and Lyapunov stability. MATLAB simulations in control system design.

Fourier transforms; distribution theory; Gibbs phenomena; Shannon sampling; Poisson sums; discrete and fast Fourier transforms; Laplace transforms; z-transforms; the uncertainty principle; Hilbert transforms; computation of inverse transforms by contour integration; stability and realization theory of linear, time invariant, continuous and discrete systems.

This course will deal with the main aspects of applied modeling and optimization suitable for engineering, science, and business students. Sample applications to be used as case studies include channel capacity computation (information theory), statistical detection and estimation (signal processing), sequential decision making/revenue maximization (business), and others. Topics will include introduction to convex and non-linear optimization and modeling; linear, quadratic, and geometric program models and applications; stochastic modeling; combinatorial issues; gradient techniques; machine learning algorithms; stochastic approximation; genetic algorithms; and ant colony optimization.

This course is designed to enhance ECE’s students knowledge in core subjects with the ability of analyzing big data applications. It will cover both the computational techniques, and the mathematical intuitions in the skill sets for the big data analytics. This class will provide students with the necessary data engineering processing skills, refined data optimizations for feature engineering, and sophisticated linear analysis for data transform and model ensembling.

This course will provide a comprehensive introduction on deep learning techniques used by practitioners in industry, with a focus on programming exercises using deep learning software packages. The course starts with a brief overview on statistics, linear algebra, and machine learning basics, and emphasizes teaching the analytical tools and the programming skills for applying deep neural networks for different application scenarios. By the end of the course, students will have a thorough knowledge on the state-of-the-art approaches used in deep learning for engineering applications.

An introduction course for machine learning theory, algorithms and applications. This course aims to provide students with the knowledge in understanding key elements of how to design algorithms/systems that automatically learn, improve and accumulate knowledge with experience. Topics covered in this course include decision tree learning, neural networks, Bayesian learning, reinforcement learning, ensembling multiple learning algorithms, and various application problems. The students will have chances to simulate their algorithms in a programming language and apply them to solve real-world problems.

This course addresses the fundamentals of wireless networking, including architectures, protocols and standards. It describes concepts, technology and applications of wireless networking as used in current and next-generation wireless networks. It explains the engineering aspects of network functions and designs. Issues such as mobility management, wireless enterprise networks, GSM, network signaling, WAP, mobile IP and 3G systems are covered.

This courses serves as a broad introduction to the several technologies and applications of wireless communications systems. The emphasis is on providing a reasonable mixture of information leading to a broad understanding of the technical issues involved, with modest depth in each of the topics. As an integrating course, the topics range from the physics of wave generation/propagation/reception through the circuit/component issues, to the signal processing concepts, to the techniques used to impress the information (voice or data) on a wireless channel, to overviews of representative applications including current generation systems and next generation systems. Upon completion of this course, the student shall understand the manner in which the more detailed information in the other three courses is integrated to create a complete system.