This course is an introduction to the foundations behind modern data analysis and machine learning. The first part of the course covers selected topics from probability theory and linear algebra that are key components of modern data analysis. Next, we cover multivariate statistical techniques for dimensionality reduction, regression, and classification. Finally, we survey recent topics in machine learning. Prerequisite: CS 2130

This class discusses solid state devices that are used for renewable energy application. While we will provide a general overview of most new and interesting technologies via lectures, discussions, and research presentations, we will focus on the detailed technical aspects of few devices namely: solar cells, thermionic devices, thermoelectric devices, solar thermal (CSPs), and batteries.

This course introduces photovoltaics and solar energy generation and gives an overview of the subject. The course will describe the operation of photovoltaic cells and efficiency improvements, industrial processes, solar thermal power generation, thin films, and nanomaterials for photovoltaics and future technologies.  Prerequisites: ECE 2200 or PHYS 2415 and APMA 2130 or MATH 3250.

Today's electronic devices are reaching nanometer dimensions where fundamental quantum and atomistic processes dominate. Instead of the traditional 'top-down' classical viewpoint in "Solid State Device" courses, quantum transport principles are needed to understand `bottom-up' how current flows through individual atoms, molecules, nanotubes or spintronic devices. This course provides a convenient starting point.Prerequisite: APMA 2130

Design and analysis of wireless communication circuits. Topics covered include transmission lines, antennas, filters, amplifiers, mixers, noise, and modulation techniques. The course is built around a semester long design project. Prerequisite ECE 2700 or ECE 3750

Quantum electronics, the study of light and matter interaction, has become the cornerstone in many areas of optical science and technology. This course reviews the principles of lasers then introduces the generalized nonlinear wave equations. This course will cover typical nonlinear effects and their applications in telecommunication, ultrafast laser, quantum computing/information and chemical/bio spectroscopy.  Prerequisite: ECE 3209.

Analyzes the measurement and behavior of high-frequency circuits and components; equivalent circuit models for lumped elements; measurement of standing waves, power, and frequency; use of vector network analyzers and spectrum analyzers; and computer-aided design, fabrication, and characterization of microstrip circuits. Corequisite: ECE 5260 or instructor permission.

Digital CMOS circuit design and analysis: combinational circuits, sequential circuits, and memory. Second order circuit issues. Global design issues: clocking and interconnect. Use of Cadence CAD tools. Team design of a significant VLSI chip including layout and implementation. Prerequisites: ECE 2330 and (ECE 2660 or ECE 2600)

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. Prerequisites: ECE 2330 or CS 2130. 

This course explores advanced embedded systems topics such as design and validation of embedded computing systems, embedded C programming, real-time operating systems for microcontrollers, safety and security, cyber-physical systems, Internet of Things, and robotics. The course includes hands-on experience, paper presentations, and discussions.  Prerequisite: ECE 3430