Unit: School of Engineering and Applied Science
Department: Department of Computer Science
Office location and address
85 Engineers WayCharlottesville, Virginia 22903
SaTC: CORE: Medium: Generalizing Adversarial Examples in Natural Language
Source: U.S. National Science Foundation (NSF)
January 01, 2022 – December 31, 2024
Content varies annually, depending on instructor interests and the needs of the department. Similar to CS 5501 and CS 7501, but taught strictly at the undergraduate level. Prerequisite: Instructor permission; additional specific requirements vary with topics.
This course is one option in the CS fourth-year thesis track. Students will seek out a faculty member as an advisor, and do an independent project with said advisor. Instructors can give the 3 credits across multiple semesters, if desired. This course is designed for students who are doing research, and want to use that research for their senior thesis. Note that this track could also be an implementation project, including a group-based project. Prerequisite: CS 2150 with a grade of C- or higher
In-depth study of a computer science or computer engineering problem by an individual student in close consultation with departmental faculty. The study is often either a thorough analysis of an abstract computer science problem or the design, implementation, and analysis of a computer system (software or hardware). Prerequisite: Instructor permission.
Required for Distinguished Majors completing the Bachelor of Arts degree in the College of Arts and Sciences. An introduction to computer science research and the writing of a Distinguished Majors thesis. Prerequisites: CS 2150 with a grade of C- or higher and CS BA major status.
This is a graduate-level machine learning course. Machine Learning is concerned with computer programs that automatically improve their performance through experience. This course covers introductory topics about the theory and practical algorithms for machine learning from a variety of perspectives. Topics include supervised learning, unsupervised learning and learning theory. Prerequisite: Calculus, Basic linear algebra, Basic Probability and Basic Algorithm. Statistics is recommended. Students should already have good programming skills.
Course content varies by section and is selected to fill timely and special interests and needs of students. See CS 7501 for example topics. May be repeated for credit when topic varies. Prerequisite: Instructor permission.
A graduate student returning from Curricular Practical Training can use this course to claim one credit hour of academic credit after successfully reporting, orally and in writing, a summary of the CPT experience to his/her academic advisor.
Detailed study of graduate course material on an independent basis under the guidance of a faculty member.
Formal record of student commitment to project research for the Master of Computer Science degree under the guidance of a faculty advisor.
Special Topics in Computer Science
For master's students who are teaching assistants.
Formal record of student commitment to thesis research for the Master of Science degree under the guidance of a faculty advisor. May be repeated as necessary.
For doctoral students who are teaching assistants.
Formal record of student commitment to doctoral research under the guidance of a faculty advisor. May be repeated as necessary.