This course focuses on the core principles of RL. Like statistical learning, a central challenge of RL is to generalize learned capabilities to unseen environments. However, RL faces additional challenges such as exploration-exploitation tradeoff, credit assignment, and distribution mismatch between behavior and target policies. Throughout the course, we will delve into various solutions to these challenges and provide theoretical justifications.

This course is designed to develop cross-competency in the technical, analytical, and professional capabilities necessary for the emerging field of Cyber-Physical Systems (CPS). It provides convergence learning activities based around the applications, technologies, and system designs of CPS as well as exploring the ethical, social, and policy dimensions of CPS work. The course also emphasizes the importance of communication as a necessary skill.

This course provides an overview of the state of the art in software analysis including static and dynamic analysis techniques and verification and validation. It explores the various ways that the analyses are used to predict software behavior. The applications include inference, symbolic execution, fault localization, model checking, security and performance. The course combines theory with practical implementation and usage. Prerequisites: CS 3240.

A graduate student returning from Curricular Practical Training can use this course to claim one credit hour of academic credit after successfully reporting, orally and in writing, a summary of the CPT experience to his/her academic advisor.

Detailed study of graduate course material on an independent basis under the guidance of a faculty member.

Analyzes network topologies; backbone design; performance and queuing theory; data-grams and virtual circuits; technology issues; layered architectures; standards; survey of commercial networks, local area networks, and contention-based communication protocols; encryption; and security. Course equivalent to ECE 7457. Prerequisite: CS 6456 or instructor permission.

Detailed study of graduate course material on an independent basis under the guidance of a faculty member.

Formal record of student commitment to project research for the Master of Computer Science degree under the guidance of a faculty advisor.

For master's students who are teaching assistants.

Formal record of student commitment to thesis research for the Master of Science degree under the guidance of a faculty advisor. May be repeated as necessary.