400

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.