Unit: School of Engineering and Applied Science
Department: Department of Computer Science
Office location and address
Rice Hall, Room 522Charlottesville, Virginia 22903
Introduces artificial intelligence. Covers fundamental concepts and techniques and surveys selected application areas. Core material includes state space search, logic, and resolution theorem proving. Application areas may include expert systems, natural language understanding, planning, machine learning, or machine perception. Provides exposure to AI implementation methods, emphasizing programming in Common LISP. Prerequisite: CS 2150 with grade of C- or higher.
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 or CS 2501 topic DSA2 with a grade of C- or higher, and BSCS major
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.
Analyzes concepts in algorithm design, problem solving strategies, proof techniques, complexity analysis, upper and lower bounds, sorting and searching, graph algorithms, geometric algorithms, probabilistic algorithms, intractability and NP-completeness, transformations, and approximation algorithms. Prerequisite: CS 4102 or equivalent.
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.
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.