This course introduces a basic grounding in designing and implementing cloud systems. It aims to acquaint students with principles and technologies of server clusters, virtualized datacenters, Internet clouds, and applications. Students will gain hands-on experience on public cloud such as Amazon EC2. Prerequisites: CS2150 Program and Data Representation or CS 111x Introduction to Programming, CS 4457 Computer Networks or equivalent background.

Course content varies by section and is selected to fill timely and special interests and needs of students. See CS 7501 for example topics. May be repeated for credit when topic varies. Prerequisite: Instructor permission.

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.

This course is one option in the CS fourth-year thesis track. Students will seek out a faculty member as an advisor, and do an independent project with said advisor. Instructors can give the 3 credits across multiple semesters, if desired. This course is designed for students who are doing research, and want to use that research for their senior thesis. Note that this track could also be an implementation project, including a group-based project. Prerequisite: CS 3140 with a grade of C- or higher, and BSCS major.

This 'acclimation' seminar helps new graduate students become productive researchers. Faculty and visitors speak on a wide variety of research topics, as well as on tools available to researchers, including library resources, various operating systems, UNIX power tools, programming languages, software development and version control systems, debugging tools, user interface toolkits, word processors, publishing systems, HTML, JAVA, browsers, Web tools, and personal time management. Prerequisite: CS graduate student or instructor permission.

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.

An overview of computer science education for undergraduate students. Topics include ethics, diversity, tutoring and teaching techniques, and classroom management. Students enrolled in this course serve as a teaching assistant for a computer science course as part of their coursework.

This is a core Cyber Physical Systems (CPS) class. It provides fundamental core material in signal processing, machine learning, and feedback control. However, the material is not presented in a traditional manner and does not replace deep domain expertise in these topics. Rather, the principles and skills taught in this class highlight the intersection of the cyber and the physical.

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.

In-depth study of a computer science or computer engineering problem by an individual student in close consultation with departmental faculty.