Introduction to object-oriented programming in a 3D graphical programming environment. First-time exposure to common programming language constructs. Project planning and storyboarding. Hands-on approach with several projects focused on the design of computer games and animation. This course has a laboratory component.

Foundations of computer science and programming. Using common programming language constructs to manipulate text, images, audio and video, and create graphical user interfaces. Hands-on approach with several projects focused on the design of programs for manipulating various kinds of multimedia. This course has a laboratory component.

Smart phone hardware, operating systems and applications together with a review of current and emerging wireless Technologies. Accompanying lab is required.

Selected topics in Computer Science. This course may or may not have a laboratory component.

Fundamental concepts of object-oriented programming. Syntax and semantics of Java, an object-oriented programming language. Principles of program design and algorithm development strategies. Classes, abstract data types, arrays, flow control, modular decomposition, overloading, exception handling, debugging, I/O, applets. This class has a laboratory component that encourages inquiry-based learning.

Discrete mathematics for computer science, including elementary propositional and predicate logic, sets, relations, functions, counting, elementary graph theory, proof techniques including proof by induction, proof by contradiction, and proof by construction.

 

CS 302 is a continuation of CS 201 and emphasizes concepts of object oriented software design. Topics include inheritance, recursion algorithm analysis, sorting algorithms, graphs, hash tables, and linked list data structures such as stacks, queues, and binary trees. This course has a laboratory component.

Efficient design of data structures, recursive algorithms, algorithms for sorting and searching, complexity analysis of algorithms, applications of algorithms and data structures in problems, state spaces, and search strategies in artificial intelligence. This course has a laboratory component.

Syntax, semantics, and concepts of C++ programming, templates, parametrized classes, generic programming, standard template library.

Object-Oriented Programming. Complex data structures. Closures. Ties. Module development. CGI, DBI and other third-party modules. mod_perl. Networking. Persistent data. Interfacing with other languages.

Unix architecture, concepts, and principles; shell concepts and principles filters, I/O redirection, environment, process management, runtime architecture.

Project oriented hands-on approach. Mandatory first day of class.

Project oriented hands-on approach.

Selected Topics in Computer Science.

Selected Topics in Computer Science.

Selected Topics in Computer Science.

Selected readings, research and project development under the direction of a faculty member.

Lexical and syntactical scan, semantics, code generation and optimization, dataflow analysis, parallelizing compilers, automatic compiler generation.

This course introduces the principles of multimedia databases including multimedia information processing, modeling, and retrieval. The media to be considered include text, image, audio and video. At the conclusion of this course, the students should understand what multimedia data retrieval is, the principles, which allow the location of relevant information from amongst a large corpus of multimedia data, and the applications of multimedia information retrieval. The students should also have the expertise and competence to design and implement retrieval software for multimedia data.

Computer Systems addressed in this course primarily are web based systems and capacity planning is a principal theme. However, the queueing theory and statistical analysis approaches are applicable to conventional computing systems and, in fact, modeling of these latter constitute relevant background information that is developed and exploited for web systems analysis.

Introduction to distributed systems, distributed hardware and software concepts, communication, processes, naming, synchronization, consistency and replication, faulty tolerance, security, client/server computing, web technologies, enterprise technologies.

Internal design and operation of a modern operating system, including interrupt handling, process scheduling, memory management, virtual memory, demand paging, file space allocation, file and directory management, file/user security and file access methods.

Remote procedure call and client-server mechanisms. Protocol definition and compilation; client and server stubs and application code; transport independence; multiple client and server systems. Applications, e.g., remote database query and update and image filtering and archiving; systems programming and file systems contexts.

Overview of all aspects of media forensics inclunding analysis of character encoding, file formats and digital media, examination of disk acquisition and duplication techniques and application of these techniques in criminal investigations scenarios.

Introduction to computational methodologies in Bioinformatics.

Introduction to Probability and Statistics with applications in Computer Science. Counting, permutations and combinations. Probability, conditional probability, Bayes Theorem. Standard probability distributions. Measures of central tendency and dispersion. Central Limit Theorem. Regression and correlation. Hypothesis testing. Random number generation. Random algorithms. Estimating probabilities by simulation. Genetic algorithms.

Introduction to natural language and computational linguistics; topics include part-of-speech tagging, syntactic parsing, semantic analysis, speech recognition, machine translation, sequence labeling algorithms, n-gram language models, statistical parsing, grammar formalisms and tree banks.

Graphics architectures, geometric transforms, 3-D, object models, shading, intensity, hidden elements, color, advanced topics.

Advanced Computer Graphics techniques aimed at "scientific visualization" applications.

Foundations for computer modeling and simulation, with accent on discrete systems: random number and process generation; statistical bases with probability and frequency distribution orientation; Monte Carlo experimentsand general purpose modeling, e.g., in SLX.

Special Topics in Computer Science.

Special Topics in Computer Science.

Participation in research seminar directed by a faculty member.

 A Capstone course comprising a series of lectures spanning the CIS curriculum by faculty and guest lectures on key topics in computer science together with lecture and in-depth discussion on computer ethics: further lectures and material on scientific inquiry, quantitative literacy and problem solving, all as they relate to the computer science field. Students take the Major Field Test in Computer Science as a requirement for completing this course. Writing is a significant part of this course, including term papers, and other significant writing assignments.

Computer Science Major Elective - 200 level