Analyzes modern software engineering practice for multi-person projects; methods for requirements specification, design, implementation, verification, and maintenance of large software systems; advanced software development techniques and large project management approaches; project planning, scheduling, resource management, configuration control, and documentation. Prerequisite: CS 3140 with a grade of C- or better

Introduces discrete mathematics and proof techniques involving first order predicate logic and induction. Application areas include sets, tuples, functions, relations, and combinatorial problems. Prereq: Must have completed CS 1110 or CS 1111 or CS 1112 or CS 1113 with a grade of C- or better OR successfully completed the CS 1110 or CS 2100 place out test.

Human-computer interaction and user-centered design in the context of software engineering. Examines the fundamental principles of human-computer interaction. Includes evaluating a system's usability based on well-defined criteria; user and task analysis, as well as conceptual models and metaphors; the use of prototyping for evaluating design alternatives; and physical design of software user-interfaces, including windows, menus, and commands. Prerequisite: CS 2100 with a grade of C- or better OR successfully completed the CS 2100 place out test.

A first course in communication networks for upper-level undergraduate students. Topics include the design of modern communication networks; point-to-point and broadcast network solutions; advanced issues such as Gigabit networks; ATM networks; and real-time communications.  Prerequisite: CS 3130 with a grade of C- or better.

Content varies annually, depending on instructor interests and the needs of the department. Similar to CS 5501 and CS 7501, but taught strictly at the undergraduate level. Prerequisite: Must have completed CS 2100 with a grade of C- or better. Additional specific requirements vary with topics.

An introduction to machine learning: the study of algorithms that improve their performance through experience. Covers both machine learning theory and algorithms. Introduces algorithms, theory, and applications related to both supervised and unsupervised learning, including regression, classification, and optimization and major algorithm families for each. Prerequisites: CS 3100 with a grade of C- or better. Background in topics covered in Probability and Linear Algebra is required.

A second course in computing with an emphasis on foundational data structures and program analysis. The course provides a introduction to object oriented programming and the Java programming language, concurrency, and inheritance / polymorphism. Additionally, foundational data structures and related algorithms / analysis are studied. These include lists, stacks, queues, trees, hash tables, and priority queues. Prereq: CS 1110 or CS 1111 or CS 1112 or CS 1113 or place out test for CS 1110 or CS 2100  

A first course in programming, software development, and computer science. Introduces computing fundamentals and an appreciation for computational thinking. No previous programming experience required. Note: CS 1110, 1111, 1112, 1113, and 1120 provide different approaches to teaching the same core material; students may only receive credit for one of these courses. Students may not enroll if CS 2100 or CS 3140 has been completed.

Mobile computing devices have become ubiquitous in our communities. In this course, we focus on the creation of mobile solutions for various modern platforms, including major mobile operating systems. Topics include mobile device architecture, programming languages, software engineering, user interface design, and app distribution. Prerequisite: CS 3140 with a grade of C- or better

Introduces artificial intelligence. Covers fundamental concepts and techniques and surveys selected application areas. Core material includes state space search, logic, and resolution theorem proving. Application areas may include expert systems, natural language understanding, planning, machine learning, or machine perception. Provides exposure to AI implementation methods, emphasizing programming in Common LISP. Prerequisite: CS 3100 with a grade of C- or better