A graduate course in computer algorithms. Includes algorithms for sorting, optimization, scheduling, and data management; graph algorithms; random algorithms; NP-completeness. Prerequisites include close familiarity with data structures, probability theory, and linear algebra.

A graduate version of ECE 559. Groups of students encouraged to work on VLSI chip designs tied into VLSI research in the Electrical and Computer Engineering or Computer Science departments. Involves knowledge of some additional aspects of computer architecture, circuit design, computer arithmetic, or a particular application area such as digital signal processing, control, cryptography, or computer graphics. Use of the chip within an overall sytem also stressed.

Not available at this time

This course will define qubits and qubit logical gates starting from fundamental quantum mechanics and quantum optics all the way up to circuit level programming of quantum algorithms run on actual quantum computers via the cloud. It is designed to introduce engineers to quantum hardware and quantum programming. Students should be familiar with vector notation of electromagnetic fields and waves, and very comfortable with linear algebra and programming in Python.

This graduate course provides an introduction to the new area of Security Engineering, and provides examples drawn from recent research at UMASS and elsewhere. Security Engineering is a multi-disciplinary field combining technical aspects of Applied Cryptography, Computer Engineering, and Networking as well as issues from Psychology, Sociology, Policy and Economics. Several guest lectures will be presented by experts in these disciplines.

Introduction to digital communications at the graduate level. Signaling formats, optimal receivers, and error probability calculations. Introduction to error control coding, source coding, and information theory. Prerequisite: undergraduate probability.

This course introduces concepts, techniques, and algorithms from artificial intelligence and machine learning, such as classification, regression, support vector machines, decision trees, neural networks, and deep neural networks. The application of these techniques to solve practical problems on computing and embedded systems is a focus of this course. Project assignments are used to reinforce concepts learned in lectures.

This course provides an overview of the core design principles used in the holistic design of low power embedded computing systems. Modern embedded system design use co-design of both hardware and software subsystems to achieve energy efficiency. The content of this class builds on foundational ECE courses in circuits, low level software design, machine learning, and signal processing. Prior courses in in at least one of these areas is recommended as a prerequisite.

Embedded systems sense, actuate, compute, and communicate to accomplish tasks in domains such as medical, automotive, and industrial controls. Informal methods of hacking together embedded systems are at odds with the criticality of their applications. This course will introduce recent developments toward more rigorous modeling and verification of embedded and cyber-physical systems.

Physical and electronic structure of semiconductors, band theory, semiconductor statistics, scattering processes and carrier transport, optical properties, modern quantum electronic devices.