CS - Computer Science

Students work with faculty adviser to complete the first phase of a capstone project.

Students work with faculty adviser to complete the second phase of a capstone project.

Introduction to computer science and programming. Representation of data in digital form. Data types, conditionals, loops, arrays and functions. Algorithmic time complexity. Creates a foundation for computer science and data driven software development. Emphasis on good design and programming techniques through practice in writing, running, and debugging programs. No programming experience expected.

Create a foundation for computer science and the software development process. Emphasis on good design and programming techniques through practice in writing, running, and debugging programs. Study of a programming language which incorporates objects, structured control statements, classes, inheritance, strong data typing, and sub-programs with parameters. No programming experience expected.

A non-required laboratory for CS 203. This course focuses on reinforcing basic programming techniques and skills. Students who are entering CS 203 with no previous programming experience will benefit most from this course. Course is graded P/NP.

Laboratory to support CS 203.

Credit arranged.

Continue to build a computer science foundation. Study of intermediate programming language constructs: event handling, graphical user interfaces, threads, and networking. Introduction to the software engineering process and programming-in-the-large.

Continues the study of computer science and software engineering methodologies with the C programming language. Analysis of common data structures, time and space efficiency, stacks, queues, linked lists, trees, graphs, hash tables, recursion, searching, and sorting algorithms.

Design, analysis and correctness proofs of important algorithms from areas such as combinatorics, seminumerical algorithms, data storage and retrieval, systems programming, and artificial intelligence. Includes a study of complexity theory.

Introduction to boolean and sequential logic. Introduction to computer system hardware including Arithmetic Logic Unit (ALU), main memory, cache memory, I/O devices, busses and interfaces, control unit, addressing techniques, and the MIPS assembly language.

Functions, structure, design, and problems of operating systems. Concepts and principles of operating system design and implementation including file system, CPU scheduling, memory management (including virtual memory), deadlocks in computer systems, concurrent processes and programming, threads, and protection.

Software lifecycle models. Requirements engineering. Planning and managing software projects. Software design methods. System integration, software quality assurance, testing, and validation. Software maintenance.

Comparative analysis of several modern high level languages in terms of data types and control structures, with emphasis on run-time behavior of programs.

Introduction to finite automata, Turing machines, formal languages, and computability.

Lexical analysis, syntactic analysis, type checking, and code generation. Introduction to optimization.

Focus on applicable skills to a career in software engineering or a related field including software entrepreneurship, professional development, and employment.

In-depth study of professional responsibility in the field of computer science. Students are expected to read journal papers, articles, and books, participate in class discussions, and give presentations.

In-depth study of professional responsibility in the field of computer science. Students are expected to read journal papers, articles, and books, participate in class discussions, and give presentations. Students will also research a current topic, and make the presentation to an audience outside the University. Students may not receive credit for both CS 368 and CS 369.

Laboratory to support CS 301.

This laboratory introduces Unix/Linux commands and tools for software development and testing. Includes scripting languages. 

The course focuses on developing and practicing techniques for rapid programming in a small team environment: approaches to problem assessment, selection of data structures and algorithms, implementation, and testing. Students will hone their skills by working in small teams to produce correct solutions to a wide variety of computing problems under time constraints. Course is graded Pass / No Pass.

The fundamentals of artificial intelligence. Topics include: heuristic search, adversarial search, local search, knowledge-based systems, artificial neural networks, reinforcement learning, AI history and philosophy of minds.

Algorithmic and analysis techniques for biological data such as DNA, RNA, proteins, and gene expression. Topics include molecular biology, alignment and searching algorithms, sequence evolution algorithms, genetic trees, and analysis of microarray data. This course is interdisciplinary and assumes programming skills.

Concepts in robotics including state estimation, filtering, perception, localization, and mapping. Introduction to various topics in computer vision.  Methods for robotic control and learning. Current topics in applied robotics. Fee: $50

A study of "smart," interconnected devices with myriad sensing capabilities, known as the "Internet of Things" (IoT). Today, IoT exists in our home appliances, automobiles, airplanes, and on our wrists - tracking how we exercise, and measuring and analyzing our sleep. Topic includes IoT technologies, architectures, protocols, data storage, and IoT security and privacy. Fee: $50

Learn about UX (User Experience) Design to gain practical insights into staying focused on the right work for the right people. Areas of study include: user research, information architecture, interaction design, and information design.

In-depth survey of basic and advanced concepts of machine learning. Topics include: linear discrimination, supervised, unsupervised, semi-supervised learning, multilayer perception, convolution neural networks, maximum-margin methods, Monte-Carlo, and reinforcement learning. Knowledge of linear algebra and vector calculus also recommended. 

An examination of topics in computer graphics, including graphical output devices, line-drawing and clipping algorithms, representation and drawing of curves, techniques for transforming graphical images, and methods of modeling and rendering in three-dimensions. 

The design and implementation of databases with an emphasis on the use of relational database management systems (DBMS). Query languages, table and index design, query evaluation, transaction management, tuning, security.

A study of architectures, algorithms and programming/debugging techniques that employ parallelism to increase performance of computer programs. Topics include parallel computer architectures, parallel programming languages for distributed and shared-memory multiprocessors and code optimization.

As more data becomes available, solutions are needed to store, process, extract, interpret, and visualize large amounts of data for scientific discovery and innovation. This course covers algorithms and technologies for the storage, analysis, and interpretation of large, diverse, and heterogeneous data sets.

Cloud computing is the delivery of on-demand computing resources, from applications to data centers, over the Internet with pay-as-you-go pricing. This course is a study of fundamentals and capabilities of cloud across various service models. Topics include cloud infrastructure, programming models, and security and privacy issues in cloud computing. Includes various case studies from the industry.

A broad first course in computer networks and internetworking. OSI and TCP/IP layered models, TCP/IP protocol suite, transmission media, local area networks, network and transport-layer protocols, internetworking, internet addressing and routing.

This course will provide an introduction to the field of computer game design. The philosophy, objectives, and history of this field will be explored. In addition, the course will emphasize practical applications of some of the more prevalent techniques.

Contemporary topics in Cybersecurity. Topics in this fast-moving field change from year to year, but each offering will introduce these core fundamentals: confidentiality, integrity, availability, access control, and defensive programming techniques.

A major design experience based on the knowledge and skills acquired in earlier course work and incorporating appropriate standards and multiple realistic constraints. Projects have some combination of the following characteristics: realism, communication, exposure, teamwork, learning, and related opportunities. 

Continuation of a major design experience based on the knowledge and skills acquired in earlier course work and incorporating appropriate standards and multiple realistic constraints. Projects have some combination of the following characteristics: realism, communication, exposure, teamwork, learning, and related opportunities. 

Selected study or project in computer science for upper-division students. Must be arranged between the student and an individual faculty member and subsequently approved by the dean of engineering. No more than three hours of directed study taken at the University may be used for elective credits to satisfy degree requirements.

Credit arranged.

Credit arranged.

Faculty-directed student research. Before enrolling, a student must consult with a faculty member to define the project. May be repeated for credit.

Research, study, or original work under the direction of a faculty mentor, leading to a scholarly thesis document with a public presentation of results. Requires approval of thesis director, department chair, dean, and the director of the honors program, when appropriate.

Applications of artificial intelligence including advanced topics. Topics include: inference, knowledge representation, search, cognitive architecture, decision making under uncertainty, and machine learning. Special project required. Knowledge of data structures required.

Study of advanced algorithmic and analysis techniques for biological data such as DNA, RNA, proteins and gene expression. Topics and project include molecular biology, alignment and searching algorithms, sequence evolution algorithms, genetic trees and analysis of microarray data. Interdisciplinary course that assumes programming skills. Special project required. Knowledge of data structures, biology, programming and calculus required.

Concepts in robotics including state estimation, filtering, perception, localization, and mapping. Introduction to various topics in computer vision. Methods for robotic control and learning. Current topics in applied robotics. Special project required. Knowledge of statistics, calculus, and data structures required.

A study of “smart,” interconnected devices with myriad sensing capabilities, known as the “Internet of Things” (IoT). IoT exists in our appliances, automobiles, airplanes, and on our wrists -- tracking how we exercise, and measuring our sleep. Topic includes IoT technologies, architectures, protocols, data storage, and IoT security and privacy. Special project required. Knowledge of data structures required.

Learn about UX (User Experience) Design to gain practical insights into staying focused on the right work for the right people. Go in-depth into areas of user research, information architecture, and information design. Other areas of study will include interaction design, visual design, accessibility, story boarding, and game mechanics. Prior experience in data structures is required.

In-depth survey of basic and advanced concepts of machine learning. Topics include: linear discrimination, supervised, unsupervised, semi-supervised learning, multilayer perception, convolution neural networks, maximum-margin methods, Monte-Carlo, and reinforcement learning.  Prior knowledge of data structures is required.  Knowledge of linear algebra and vector calculus also recommended.  Research project requiring an application of a machine learning techniques.

An examination of topics in computer graphics, including graphical output devices, line-drawing and clipping algorithms, representation and drawing of curves, techniques for transforming graphical images, and methods of modeling and rendering in three-dimensions. Special project required. Knowledge of data structures, linear algebra, and calculus are required.

A study of architectures, algorithms and programming/debugging techniques that employ parallelism to increase performance of computer programs. Topics include parallel computer architectures, parallel programming languages for distributed and shared-memory multiprocessors and code optimization. Special project required. Knowledge of data structures, computer architecture, and object oriented design required.

As more data becomes available, solutions are needed to store, process, extract, interpret, and visualize large amounts of data for scientific discovery and innovation. This course covers algorithms and technologies for the storage, analysis, and interpretation of large, diverse, and heterogeneous data sets. Special project required. Knowledge of statistics and data structures required. 

Advanced design, development, and evaluation or web-based applications. Human factors, security aspects, and databases are emphasized. Knowledge of data structures required.

Cloud computing is the delivery of on-demand computing resources, from applications to data centers, over the Internet with pay-as-you-go pricing. Study fundamentals and capabilities of cloud across various service models. Topics include cloud infrastructure, programming models, and security and privacy issues in cloud computing. Includes various case studies from industry. Special project required. Knowledge of data structures required.

Computer networks and internetworking. Specialized applications of OSI and TCP/IP layered models, TCP/IP protocol suite, transmission media, local area network and transport-layer protocols, internetworking, internet addressing and routing. Special project required. Knowledge of data structures required.

Computer game design emphasizing the philosophy, objectives, and history of this field. In addition, the course will emphasize advanced applications of some of the more prevalent techniques. Special project required. Knowledge of data structures required.

Cryptography, program security, security in operating systems, security in computer networks, security administration and policies. Special project required. Knowledge of data structures and unix required.

Credit arranged.

Credit arranged.

Credit arranged.

Faculty-directed student research. Before enrolling, a student must consult with a faculty member to define the project. May be repeated for credit.