This course will provide an introduction to modern cryptography and its applications to computer security. This course will cover the fundamentals of symmetric cryptography (i.e., encryption and message authentication) and public-key cryptography (i.e., key-exchange and signatures) as well as cryptographic protocols like zero-knowledge proof systems. Recommended prerequisites: CS 2102, 3102, and 4102 (or equivalent experience).

This is a graduate-level machine learning course. Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers introductory topics about the theory and practical algorithms for machine learning from a variety of perspectives. Topics include supervised learning, unsupervised learning and learning theory. Prerequisite: Calculus, Basic linear algebra, Basic Probability and Basic Algorithm. Statistics is recommended. Students should already have good programming skills.

Study of representative digital computer organization with emphasis on control unit logic, input/output processors and devices, asynchronous processing, concurrency, and parallelism. Memory hierarchies. Prerequisite: CS 3330 or proficiency in assembly language programming.

Focuses on techniques for designing and analyzing dependable computer-based systems. Topics include basic dependability concepts and attributes, fault models and effects, combinatorial and state-space modeling, hardware redundancy, error detecting and correcting codes, time redundancy, software fault tolerance, checkpointing and recovery, reliable networked systems, error detection techniques, and experimental dependability evaluation techniques.Prerequisites: A basic knowledge of probability and computer architecture is required. A working knowledge of programming is required for homework and mini projects.

Interactions between robots and humans are influenced by form, function and expectations. Quantitative techniques evaluate performance of specific tasks and functions. Qualitative techniques are used to evaluate the interaction and to understand expectations and perceptions of the human side of the interaction. Students use humanoid robots to develop and evaluate interactions within a specific application context.

Cyber-physical systems (CPS) are smart systems that include co-engineered interacting networks of physical and computational components. This course will teach students the required skills to analyze the CPS that are all around us, so that when they contribute to the design of CPS, they are able to understand important safety and security aspects and feel confident designing and analyzing CPS systems.

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.

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.