|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
Survey of the field of usable security and privacy with an emphasis on emerging technologies. Topics include authentication, location privacy, social network privacy, behavioral advertising, health privacy, anonymity, cryptocurrency, technical writing and ethical conduct of usable privacy and security research. Students are expected to have completed coursework in at least one of: software development, human factors, experimental psychology, security policy, or computer security, before enrolling in this course.
-
3.00 Credits
An introductory computer graphics class intended for students interested in computer simulation for modeling and animation. Course material covers topics related to physically-based modeling and dynamic simulation techniques as used for the automatic synthesis of motion and geometry for animation and computer graphics. A variety of approaches are explored, with a special emphasis on the use of particle-systems, rigid bodies, cloth and modeling other natural phenomena. Students are expected to have familiarity with computer graphics before enrolling in this course.
-
3.00 Credits
Covers principles of information systems security, including security policies, cryptography, authentication, access control mechanisms, system evaluation models, auditing, and intrusion detection. Computer security system case studies are analyzed. Students are expected to have completed coursework in operating systems and networking before enrolling in this course.
-
3.00 Credits
Covers topics related to the administration and security of computer systems. Primary emphasis is on the administration and security of contemporary operating systems. Students are expected to have completed coursework in operating systems and networking before enrolling in this course.
-
3.00 Credits
Overview of programming language structures and features and their implementation. Control and data structures found in various languages are studied. Also includes runtime organization and environment and implementation models. Students are expected to have completed coursework in assembly language and formal language theory before enrolling in this course.
-
3.00 Credits
Course covers applied methods and techniques in Data Science, including data scraping, cleaning, and storage; technical issues when working with different types of data; basic topics in machine learning; parallel and distributed computing; cloud computing; data visualization; and ethical issues in Data Science. Students are expected to have completed at least one college-level introductory class in statistics before enrolling in this course.
-
3.00 Credits
This course presents fundamental concepts in Artificial Intelligence. Specific topics include uninformed and informed search techniques, game playing, Markov decision processes, reinforcement learning, uncertain knowledge and probabilistic reasoning, constraint satisfaction problems, and supervised learning. Students must be familiar with principles of probability and statistics and must have programming experience when enrolling in this course.
-
3.00 Credits
Students learn to code machine learning algorithms from basic principles, without machine learning libraries. Topics include supervised learning such as regression and classification; unsupervised learning, such as clustering; and measures of performance such as bias/variance theory, measures, and error metrics. Students must be familiar with principles of probability and statistics and must have programming experience when enrolling in this course.
-
3.00 Credits
This course covers the background of cloud computing, the technical knowledge needed to build applications in the cloud, and the hardware and software systems for architecting a cloud application environment. Cloud services are utilized. Students learn cloud computing skills and apply these skills in realistic projects. Students are expected to have completed coursework in networking or operating systems before enrolling in this course.
-
3.00 Credits
Introduction to the methods and problems of computational science. Uses problems from engineering and science to develop mathematical and computational solutions. Case studies use techniques from Grand Challenge problems. Emphasizes the use of networking, group development, and modern programming environments. Students are expected to have completed coursework in calculus and linear algebra before enrolling in this course.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2024 AcademyOne, Inc.
|
|
|