This course will teach students the required skills, concepts, and algorithms to develop mobile robots that act autonomously in complex environments. The main emphasis is on mobile robot locomotion and kinematics, control, sensing, localization, mapping, path planning, and motion planning. Besides theory, students are exposed to simulation environments and lab exercises with real robotic systems.

This course explores the intricacies of AI hardware, including the current landscape and anticipating the necessary developments in response to AI's rapid growth and widespread integration across all computing tiers. Through this exploration, you will gain an understanding of both the existing technologies and the future challenges in AI hardware design and implementation.

A first-level graduate course covering a topic not normally covered in the graduate course offerings. The topic will usually reflect new developments in the electrical and computer engineering field. Offering is based on student and faculty interests. Prerequisite: Instructor permission.

This one-hour weekly seminar course features presentations given by ECE faculty members, to introduce various research areas, topics, and advances in Electrical and Computer Engineering.  It is a one-credit course required for all first-year ECE graduate students. 

Optoelectronics merges optics and microelectronics. Optoelectronic devices and circuits have become core technologies for several key technical areas such as telecommunications, information processing, optical storage, and sensors. This course will cover devices that generate (semiconductor light emitting diodes and lasers), modulate, amplify, switch, and detect optical signals. Also included are solar cells, photonic crystals, and plasmonics.

Topics include probability spaces; random variables and vectors; and random sequences and processes; especially specification and classification. Includes detailed discussion of second-order stationary processes and Markov processes; inequalities, convergence, laws of large numbers, central limit theorem, ergodic, theorems; and MS estimation, Linear MS estimation, and the Orthogonality Principle. Prerequisite: APMA 3100, MATH 3100, or equivalent.

Covers foundations of estimation theory and machine learning in a probabilistic modeling framework. Topics include frequentist and Bayesian estimation, analysis of estimators, linear regression, linear classification, graphical models, Markov models, sampling methods, and variational inference. Requires APMA 3100 or an equivalent course on Probability, familiarity with linear algebra, and Python programming.

A first graduate course in digital signal processing. Topics include discrete-time signals and systems, application of z-transforms, the discrete-time Fourier transform, sampling, digital filter design, the discrete Fourier transform, the fast Fourier transform, quantization effects and nonlinear filters. Additional topics can include signal compression and multi-resolution processing.

This course focuses on an in-depth study of advanced topics and interests in image data analysis. Students will learn practical image techniques and gain mathematical fundamentals in machine learning needed to build their own models for effective problem solving. The graduate students (ECE/CS 6501) will be given additional programming tasks and more advanced theoretical questions.

This course aims to provide an instruction to basic principles and tools for the analysis and design of control systems. It is intended for general graduate students in engineering and science. Topics to be covered include concepts, examples and designs of feedback, system modeling, linear and nonlinear dynamic behaviors, stability analysis, frequency domain analysis and design, transfer functions, PID control, and robustness of control systems.