Supports the writing of the technical report component of the fourth-year thesis, credit for which is given in STS 4600. Students will write the report assuming a non-technical audience. The course is part of the CS 4XXX elective option in the fourth-year CS thesis track. BS CS 4th years (both first & second majors) and pre- or co-requisite STS 4500

In-depth study of a computer science or computer engineering problem by an individual student in close consultation with departmental faculty. The study is often either a thorough analysis of an abstract computer science problem or the design, implementation, and analysis of a computer system (software or hardware). Prerequisite: Instructor permission.

Required for Distinguished Majors completing the Bachelor of Arts degree in the College of Arts and Sciences. An introduction to computer science research and the writing of a Distinguished Majors thesis. Prerequisites: CS 2150 or CS 2501 topic DSA2 with a grade of C- or higher, and BSCS major

Provide a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency for non-CS, non-CpE students. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security). Prerequisite: CS 5010, CS 1110 or equivalent, Math 1210 or equiv, Math 3351 or equiv, Math 3100 or equiv.

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.

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.

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.