91710

CMSC 118 INTRO TO Computing:Digital Humanities

Rebecca Thomas

. T . Th .

3:10 pm -4:30 pm

RKC 100

MATC

Cross-listed: Experimental Humanities 4 credits What, if anything, can we learn by applying basic computing to works of literature? In this course, we'll apply techniques from Natural Language Processing (the subdiscipline of computer science that deals with information in text form) to all kinds of texts. We’ll identify hapax legomena (words that appear only once in a given text) and think about whether and when they are significant. We'll see how scholars try to use statistical techniques to approach disputes over authorship. Each student will work alone or with a small group to pose an interesting question about a specific text or corpus and to construct computational tools for beginning to address the question. No prerequisite. Class size: 20

91711

CMSC 131 Foundations of Mind, Brain, and Behavior

Sven Anderson Lab:

M . W . . . . . . F

8:30-9:50 am 8:30- 10:30 am

RKC 100 RKC 107

SCI

Cross-listed: Mind, Brain & Behavior; Philosophy; Psychology How do brains make minds? Can computers think? Is my dog conscious? Cognitive science assumes that the brain is some sort of computational engine, and, beginning with that premise, attempts to find answers to such questions. This course will be taught by faculty from biology, computer science, linguistics, philosophy, and psychology, who will combine their different approaches to explore how humans and other intelligent systems feel, perceive, reason, plan, and act. In particular, the course will focus on the fundamental importance of language, signaling, and representation at many levels, from the neural to the organismal. Laboratories will provide students with hands-on experience analyzing neural and behavioral data as well as with computational modeling. Prerequisites: pre-calculus or its equivalent and a willingness to engage a broad variety of ideas and approaches from the natural, mathematical, and social sciences. Class size: 20

91712

CMSC 143 Object-Oriented Programming with Robots

Keith O'Hara Lab:

. T . Th . . . . . F

8:30-9:50 am 10:30-12:30 pm

OLIN LC 115 RKC 107

MATC

Cross-listed: Mind, Brain & Behavior This course introduces students with prior programming experience to object-oriented design and programming through the design and implementation of mobile robot programs. The programs will enable the robot to move around the world, reacting to sensors such as obstacle detectors and a color camera.  Students will learn how to move from an informal problem statement, through increasingly precise problem specifications, to design and implementation of a solution.  Good programming habits will be emphasized. Purchase of a small personal robot (to be specified by the instructor) is recommended. Prerequisite: any Introduction to Computing course, or permission of the instructor. Class size: 24

91713

CMSC 201 Data Structures

Rebecca Thomas Lab:

. T . Th . . . . . F

10:10-11:30 am 10:30-12:30 pm

RKC 100 RKC 100

MATC

Cross-listed: Mind, Brain & Behavior This course introduces students to essential principles of program design and analysis that underlie applications of computation to internet communication, digital media, and artificial intelligence.  Building on basic programming skills, we will focus on the construction of more sophisticated and reliable computer programs that employ the most important data structures.  Data structures, common ways in which data is organized and manipulated, are an important aspect of modern programs.  Consequently, throughout the course students will learn to create and use the most useful data structures, including files, lists, stacks, trees, and graphs. Students will write several programs, ranging from short lab assignments to larger systems of their own design. Prerequisite: CMSC 141 or 143, or permission of the instructor. Class size: 20

91714

CMSC 317 The Computational Image

Keith O'Hara

. T . Th .

1:30 pm -2:50 pm

RKC 100

MATC

This course covers computational techniques for the analysis and synthesis of digital images.  Using algorithms and approaches from computational geometry, computer graphics, image processing, computer vision, and augmented reality, students will build computer systems that are visually interactive. This course covers topics such as image formation, feature extraction, object segmentation, recognition, and tracking, rendering, and multi-view geometry. Prerequisite: CMSC 201 or permission of the instructor. Class size: 16

91715

CMSC 351 Artificial Intelligence

Sven Anderson

M . . . F

1:30 pm -2:50 pm

RKC 107

MATC

Cross-listed: Mind, Brain & Behavior 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 142.

Class size: 16