CRN

94135

Distribution

E/G

Course No.

CMSC 141 A Q course

Title

Computer Science I

Professor

Robert McGrail

Schedule

Mon Wed 8:30 am - 9:50 am Henderson 101A

This course will introduce the notion of a computational process as well as the idea of a program as a director of such processes. The study of problem-solving techniques and algorithm development will prepare students to apply the syntax and structure of a programming language to a variety of problem statements. The course will include regular programming assignments as well as a programming project.

Prerequisite: Eligibility for Q courses.

CRN

94136

Distribution

E/G

Course No.

CMSC 141 B Q course

Title

Computer Science I

Professor

Rebecca Thomas

Schedule

Mon Wed 10:00 am - 11:20 am Henderson 101A

See above.

CRN

94137

Distribution

E/G

Course No.

CMSC 142 Q course

Title

Computer Science II

Professor

Robert McGrail

Schedule

Tu Th 8:30 am - 9:50 am Henderson 101A

This course is a continuation of Computer Science 141. Elementary data structures, such as lists, records, and trees, will be discussed, as will the essentials of sorting algorithms and algorithm analysis. The inclusion of other topics such as error handling and other control features will be subject to instructor whim.

Prerequisite: Computer Science 141 or its equivalent.

Corequisite: Mathematics 111.

CRN

94138

Distribution

E/G

Course No.

CMSC 273

Title

Scientific Programming

Professor

Robert McGrail & Matthew Deady

Schedule

Mon Wed 1:30 pm - 2:50 pm Rose 113

This course introduces numerical methods and programming languages used in the physical sciences. Common techniques of data analysis, approximation, quadrature, and numerical solution of differential equations are developed for their own sake and also as an introduction to available analysis packages. This semester the C language will be used, with some time spent on FORTRAN.

Prerequisite: some previous programming experience.

CRN

94139

Distribution

E

Course No.

CMSC 351

Title

Artificial Intelligence

Professor

Rebecca Thomas

Schedule

Tu Th 3:00 pm - 4:20 pm ROSE 113

This course provides a broad introduction to topics in artificial intelligence, including knowledge representation and reasoning, planning and problem solving, and machine learning. Advanced topics may include natural language processing, multi-agent systems, image processing, or other topics of the instructor's choice.

Prerequisite: Computer Science 141 or its equivalent.