Exploration of a topic (to be determined) not covered by the standard curriculum but of interest to faculty and students in a particular semester.

Principles and practice of programming. Computing fundamentals: variables, types, scope, objects, classes, functions, parameter passing, input/output, conditionals, and iteration. Defensive programming techniques, including exceptions. Program testing and debugging. Includes programming laboratory.

Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the SU academic department to assign the appropriate course level, title, and grade for the student's transcript.

Exploration of a topic (to be determined) not covered by the standard curriculum but of interest to faculty and students in a particular semester.

Methods for exploratory data analysis. Graphical and numerical summaries of numerical and categorical data. Drawing conclusions from data. Basic probability, correlation, estimation and testing. Concepts of study design.

Introduction to key computer-science concepts through functional programming. Recursion, data representation, data abstraction, and computational patterns. Algebraic data types and higher-order functions. Models of computation. Three hours of lecture and one hour of computer laboratory.

Participation in a discipline or subject related experience. Student must be evaluated by written or oral reports or an examination. Permission in advance with the consent of the department chairperson, instructor, and dean. Limited to those in good academic standing.

Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.

Exploration of a problem, or problems, in depth. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor(s) and the department.

Exploration of a topic (to be determined) not covered by the standard curriculum but of interest to faculty and students in a particular semester.

Programming-oriented introduction to fundamentals in statistics and probability; elementary statistics, graphical and numerical representation; probability distributions; tests and confidence intervals; regression, and correlation. CPS 621 adds Journalism applications of statistical methods.

Digital logic, data types and their representations, instruction set architecture, assembly language, program construction, processors, memory hierarchy, traps and interrupts, privilege and security, input-output subsystems.

Input and output, including error handling, file descriptors and interface to OS. Pointers and pointer arithmetic. Structures. Memory functions and storage allocation. Makefiles, header files, libraries, compilation, and linking.

Abstract data structures including arrays, lists, trees, binary search trees, priority queues, graphs. Algorithm analysis. Examples include data structures used for security-related applications.

Environments, stores, scoping, functional and imperative languages, modules, classes, data encapsulation, types, and polymorphism. Implementation of these constructs in a definitional interpreter.

Participation in a discipline or subject related experience. Student must be evaluated by written or oral reports or an examination. Permission in advance with the consent of the department chairperson, instructor, and dean. Limited to those in good academic standing.

Basic set theory and symbolic logic. Methods of proof, including mathematical induction. Relations, partitions, partial orders, functions, and graphs. Credit cannot be given for both MAT 375 and CIS 375.

Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.

Fundamental design principles for computer and information systems. Concurrency, race conditions, complexity, modularity, abstraction, layering, hierarchy, iteration, emergent properties, organization, virtualization, performance, atomicity, fault tolerance, consistency, and security.

Specific topics in computer and information science suitable for honors-thesis research.

Exploration of a topic (to be determined) not covered by the standard curriculum but of interest to faculty and students in a particular semester.

Graphics programming. User interfaces. Modeling and viewing transformations. Shading techniques. Representations of three-dimensional models. Curves and non-planar surfaces. Ray tracing and radiosity. Antialiasing.

Classical cryptosystems and their cryptanalysis, RSA and other public-key cryptosystems, pseudo-random sequences, zero-knowledge protocols, related ethical and social concerns.

Algorithms for multiagent systems. Environment types for agent systems. Communications, game theoretical models, automatic auctions, utility and decision theory for multiagent systems, relationships between distributed systems and multiagent systems, Belief-Desire-Intention architecture, logic-based agent models, and agent simulations.  Additional work required for graduate students.

A variety of subjects surveyed or a particular subject in depth. Additional coursework required of graduate students.

Fundamental aspects of virtual environments: applications, methods, and technologies. Development of 3D applications such as games, animations, and immersive virtual worlds. Aspects of 3D graphics (3D transformations and physics) and interactive systems (human perception and motion tracking).

Development of applications for different mobile devices. Creating effective user interfaces, efficient use of persistent storage, network services, GPS, maps and sensors. Additional work required of graduate students.

Programming in Java with generic classes. Defining generic classes. Specifying types and inheritance. Verifying correctness of Java methods and classes. Managing concurrency, remote method invocation, and performance considerations.

Software engineering process models. Software requirements analysis, including object-oriented methodology. Behavioral and nonbehavioral requirements. Development of specification documents. Unified Modeling Language. High-level design and software architecture. Team projects.

Detailed design of software. Implementation and software testing. Developing software implementations from UML specifications and design documents. Software development methodologies, including agile development. Ethics and social responsibility. Team projects.

Data communication networks, multilayer network architecture, data transmission fundamentals, network protocols, local and wide area networks, transport and application protocols.

Knowledge representation, production systems, search algorithms, game playing, uncertainty handling, learning, automated reasoning, computer vision, and natural language processing. Programming project or term paper required for CIS 667, not for CIS 467.

Linguistic and computational aspect of natural language processing technologies. Lectures, readings, and projects in the computational techniques required to perform all levels of linguistic processing of text. Additional work required of graduate students.

Participation in a discipline- or subject-related experience. Students must be evaluated by written or oral reports or an examination. Limited to those in good academic standing.

Classical methods of minimum and maximum, gradient methods, conjugate direction methods, linear programming and simplex algorithm, integer programming, non-linear optimization, random search algorithms.

Countable and uncountable sets; diagonalization proofs; finite state automata; regular, context-free, context-sensitive, recursive, and r. e. languages; Turing machines; relationships between classes of languages and machines; the halting problem; proof methods for decidability and undecidabilty.

Mathematical modeling of computational problems; searching and sorting algorithms; search trees, heaps, and hash tables; divide-and-conquer, dynamic programming, and greedy choice design techniques; graph algorithms; cryptographic algorithms; NP-completeness; and selected topics.

Purpose of QC; simple Markov processes; quantum registers; quantum state transitions; classical vs quantum models of computation; measurement and superposition; no-cloning theorem; quantum teleportation; quantum logic.

Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.

Analytical/logical basis for trusting systems. Access requests, authorizations, certificates, credentials, jurisdiction, and delegation. Applications include distributed access control, process isolation and sharing, and access policies.

Components in Mobile OS; basic mobile app development; sandbox mechanism; permission enforcement; vulnerabilities; malware attacks. Additional work required of graduate students.

In-depth exploration of a problem or problems.  Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor or instructors and the department.

Completion of an Honors Capstone Project under the supervision of a faculty member.

Exploration of a topic (to be determined) not covered by the standard curriculum but of interest to faculty and students in a particular semester.

Programming a small compiler. Lexical analysis, tokens, finite automata, hashing. Syntax analysis, grammars, syntax trees, error recovery. Scope and type analysis, symbol tables. Run-time stack, variable addressing, expression evaluation, procedure activation, recursion. Code generation, 0ptimization, portability.

Kinematics, dynamics, and control of mobile and/or manipulator robots. Path planning, actuators, sensors, human/machine interface. Two hours lecture and two hours laboratory weekly. Design project.

Permutations, combinations, recurrence relations, generating functions, inclusion-exclusion and applications, introductory graph theory.

Organization, analysis, and documentation of a sophisticated implementation project in a prominent high-level language, such as ADA, C, or Modular-2. Substantial programming assignments and analytical documentation. Language and project may vary from year to year.

Pointers, dynamic memory management, data abstraction, classes, derived classes, inheritance, types, structures and templates. Threaded programming, standard template library, interfaces. Substantial programming assignments.

Fundamentals of the knowledge discovery and data mining process. Basics of supervised and unsupervised learning. Applications (recommendation and collaborative filtering) and computational tools for carrying out predictive/descriptive modeling. Additional work required for graduate students.

Perceptrons and the Perceptron Convergence Theorem; non-linear optimization, gradient descent methods; neural net architecture, conjugate-gradient and recurrent networks; Hopfield networks, Kohonen's feature maps; non-neural clustering algorithms.

Applications of mathematical methods to knowledge bases. Methods include nonclassical, fuzzy logic and statistical inference. Application topics include planning, temporal and physical reasoning, attitudes, the frame problem, preference, constraints, qualitative differential equations, situation theory.

Participation in a discipline or subject related experience. Student must be evaluated by written or oral reports or an examination. Permission in advance with the consent of the department chairperson, instructor, and dean. Limited to those in good academic standing.

Offered through SUAbroad by educational institution outside the United States. Student registers for the course at the foreign institution and is graded according to that institution's practice. SUAbroad works with the S.U. academic department to assign the appropriate course level, title, and grade for the student's transcript.

Processes, events, alphabets, and trace sets. Process equivalence. Divergence, dead-lock, fairness, and termination. Message channels, buffers, pipelines, trees, rings, grids, recursive nets. Mutual exclusion, semaphores, conditional critical regions, monitors, remote procedures. Programming exercises in Joyce.

Exploration of a problem, or problems, in depth. Individual independent study upon a plan submitted by the student. Admission by consent of supervising instructor(s) and the department.