Students may complete a major or minor in Computer Science or a minor in computational methods.
Douglas S. Blank, Associate Professor and Chair
Deepak Kumar, Professor
Dianna Xu, Assistant Professor
Faculty at Haverford College
John Dougherty, Assistant Professor, Program Director
Steven Lindell, Associate Professor
David G. Wonnacott, Associate Professor
Computer Science is the science of algorithms—their theory, analysis, design and implementation. As such it is an interdisciplinary field with roots in mathematics and engineering and applications in many other academic disciplines. The department at Bryn Mawr is founded on the belief that computer science should transcend from being a subfield of mathematics and engineering and play a broader role in all forms of human inquiry.
The Computer Science Department is supported jointly by faculty at both Bryn Mawr and Haverford Colleges. The department welcomes students who wish to pursue a major in computer science. Additionally, the department also offers a minor in computer science, a concentration in computer science (at Haverford College) and a minor in computational methods (at Bryn Mawr College). The department also strives to facilitate evolving interdisciplinary majors. For example, students can propose a major in cognitive science by combining coursework from computer science and disciplines such as psychology and philosophy.
All majors, minors and concentrations offered by the department emphasize foundations and basic principles of information science, rather than engineering or data-processing applications. The aim is to provide students with skills that transcend short-term trends in computer hardware and software.
Major in Computer Science
Students are encouraged to prepare a major course plan in consultation with their academic adviser in Computer Science. The requirements for a major in computer science are three introductory courses (CMSC 110 or 205, 206 and 231), three core courses (CMSC 240, 245 and one of 330, 340 or 345), six electives of a student’s choosing and a senior thesis. Students should ensure that they have completed at least three courses in computer science by the end of their sophomore year (we highly recommend CMSC 110, 206 and 231).
Minor in Computer Science
Students in any major are encouraged to complete a minor in computer science. Completing a minor in computer science enables students to pursue graduate studies in computer science, in addition to their own major. The requirements for a minor in computer science at Bryn Mawr are CMSC 110 or 205, 206, 231, any two of CMSC 240, 245, 246, 330, 340 or 345, and two electives chosen from any course in computer science, approved by the student’s adviser in computer science. As mentioned above, these requirements can be combined with any major, depending on the student’s interest and preparation.
Minor in Computational Methods
This minor is designed to enable students majoring in any discipline to learn computational methods and applications in their major area of study. The requirements for a minor in computational methods are CMSC 110 or 205, 206, 231; one of CMSC 212, 225, 245, 246, 330, 340 or 361; any two computational courses depending on a student’s major and interests (there are over 35 such courses to choose from in various departments).
Students can declare a minor at the end of their sophomore year or soon after. Students should prepare a course plan and have it approved by at least two faculty advisers. Students minoring in computational methods are encouraged to propose senior projects/theses that involve the application of computational modeling in their major field of study.
An introduction to the nature, subject matter and branches of computer science as an academic discipline, and the nature, development, coding, testing, documenting and analysis of the efficiency and limitations of algorithms. Also includes the social context of computing (risks, liabilities, intellectual property and infringement). (Blank, Division II and Quantitative Skills)
An introduction to visualization of complex data through computer manipulation. Explores the tools necessary to allow the human mind to make sense of vast amounts of data collected in many fields of study. Topics: 2D/3D representations, programming techniques, data conversion principles, color representation and introduction to virtual reality. (Allen, Division II and Quantitative Skills) Not offered in 2009-10.
Introduction to the fundamental algorithms and data structures of computer science: sorting, searching, recursion, backtrack search, lists, stacks, queues, trees, graphs, dictionaries. Introduction to the analysis of algorithms. Prerequisite: CMSC 205 or 110, or permission of instructor. (Blank, Division II)
Presents the fundamental principles of computer graphics: data structures for representing objects to be viewed, and algorithms for generating images from representations. Prerequisite: MATH 203 or 215, or permission of instructor. (Xu) Not offered in 2009-10.
(Hughes, Division II and Quantitative Skills; cross-listed as MATH B231 and PHIL B230)
A lecture/laboratory course studying the hierarchical design of modern digital computers. Combinatorial and sequential logic elements; construction of microprocessors; instruction sets; assembly language programming. Lectures cover the theoretical aspects of machine architecture. In the laboratory, designs discussed in lecture are constructed in software. Prerequisite: CMSC 206 or permission of instructor. (Kumar, Division II)
An introduction to a wide range of topics relating to programming languages with an emphasis on abstraction and design. Design issues relevant to the implementation of programming languages are discussed, including a review and in-depth treatment of mechanisms for sequence control, the run-time structure of programming languages and programming in the large. The course has a strong lab component where students get to construct large programs in at least three different imperative programming languages. (Blank, Kumar, Division II and Quantitative Skills) Not offered in 2009-10.
Topics course; course content varies. Topic for 2008-09 is Programming in UNIX and C. Provides an in-depth introduction to C and C++, as well as programming principles such as abstraction, encapsulation and modularization. Another focus of the class is to gain proficiency in the UNIX operating system. Assumes familiarity with conditionals, loops, functions and arrays and will focus on C-specific topics such as pointer manipulations, dynamic memory allocation and abstract data types. An excellent preparation for classes such as operating systems and software engineering principles and programming techniques to facilitate medium-scaled development projects. Prerequisite: CMSC 110 or 205. (Xu Blank, Division II and Quantitative Skills) Not offered in 2009-10.
This course is for students of all disciplines interested in learning the foundations of computational methods and modeling. Topics include the theory and role of computational methods in data analysis, an introduction to fundamental computation (combinatorics, probability and related statistics), and an introduction to statistical simulation and probability models, with a specific focus on Monte Carlo simulation. Examples will be drawn from numerous disciplines across the natural sciences. Two lectures and one two-hour problem session a week. (Allen, Division II and Quantitative Skills; cross-listed as BIOL B250 and GEOL B250) Not offered in 2009-10.
(Dalke, Blankenship, Division III; cross-listed as ENGL B257) Not offered in 2009-10.
An introduction to the fundamental principles of computer graphics: including 3D modeling, rendering and animation. Topics cover: 2D and 3D transformations; rendering techniques; geometric algorithms; 3D object models (surface and volume); visible surface algorithms; shading and mapping; ray tracing; and select others. Prerequisites: CMSC B110, CMSC B206, CMSC/MATH B231 and CMSC B246 or permission of instructor. (Xu) Not offered in 2009-10.
Introduction to computational models of understanding and processing human languages. How elements of linguistics, computer science, and artificial intelligence can be combined to help computers process human language and to help linguists understand language through computer models. Topics covered: syntax, semantics, pragmatics, generation and knowledge representation techniques. Prerequisite: some background in linguistics or computer science. (Kumar)
This course examines the applications of algorithms to the accomplishments of various programming tasks. The focus will be on understanding of problem-solving methods, along with the construction of algorithms, rather than emphasizing formal proving methodologies. Topics include divide and conquer, approximations for NP-Complete problems, data mining and parallel algorithms. Prerequisites: CMSC 206 and 231. (Kumar, Division II and Quantitative Skills)
A practical introduction to modern operating systems, using case studies from UNIX, VMS, MSDOS and the Macintosh. Lab sessions will explore the implementation of abstract concepts, such as resource allocation and deadlock. Topics include file systems, memory allocation schemes, semaphores and critical sections, device drivers, multiprocessing and resource sharing. (Xu)
A multidisciplinary exploration of the interactions underlying both real and simulated systems, such as ant colonies, economies, brains, earthquakes, biological evolution, artificial evolution, computers and life. These emergent systems are often characterized by simple, local interactions that collectively produce global phenomena not apparent in the local interactions. (Blank; cross-listed as BIOL B361)
Cognitive science is the interdisciplinary study of intelligence in mechanical and organic systems. In this introductory course, we examine many topics from computer science, linguistics, neuroscience, mathematics, philosophy and psychology. Can a computer be intelligent? How do neurons give rise to thinking? What is consciousness? These are some of the questions we will examine. No prior knowledge or experience with any of the subfields is assumed or necessary. Prerequisite: permission of instructor. (staff) Not offered in 2009-10.
Survey of Artificial Intelligence (AI), the study of how to program computers to behave in ways normally attributed to “intelligence” when observed in humans. Topics include heuristic versus algorithmic programming; cognitive simulation versus machine intelligence; problem-solving; inference; natural language understanding; scene analysis; learning; decision-making. Topics are illustrated by programs from literature, programming projects in appropriate languages and building small robots. (Kumar, Division II and Quantitative Skills; cross-listed as PHIL B372)
A topical course facilitating an in-depth study on a current topic in computer science. Prerequisite: permission of instructor. (Xu, Division II)
An independent project in computer science culminating in a written report/thesis and oral presentation. Class discussions of work in progress and oral and written presentations of research results will be emphasized. Required for all computer science majors in the spring semester of their senior year. (Xu)
Haverford College currently offers the following courses in Computer Science:
CMSC H100 The World of Computing
CMSC H105 Introduction to Computer Science
CMSC H187 Scientific Computing: Discrete Systems
CMSC H206 Introduction to Data Structures
CMSC H210 Linear Optimization and Game Theory
CMSC H235 Information and Coding Theory
CMSC H245 Principles of Programming Languages
CMSC H287 Advanced Topics: High Performance Scientific Computing
CMSC H345 Theory of Computation
CMSC H350 Compiler Design
CMSC H394 Advanced Topics in Theoretical Computer Science & Discrete Mathematics
CMSC H399 Senior Seminar
CMSC H480 Independent Study