Computer Science
Students may complete a minor in computer science or a minor in computational methods within selected majors. Students may submit an application to major in computer science through the independent major program.
Faculty
Douglas S. Blank, Assistant Professor
John Dougherty, Assistant Professor at Haverford College
Deepak Kumar, Professor and Coordinator
Steven Lindell, Associate Professor and Coordinator at Haverford College
David G. Wonnacott, Associate Professor at Haverford College
Dianna Xu, Assistant 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 program 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 Program is supported jointly by faculty at both Bryn Mawr and Haverford Colleges. The program welcomes students who wish to pursue a major in computer science. Additionally, the program 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 program 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 program emphasize foundations and basic principles of information science, rather than engineering or dataprocessing applications. The aim is to provide students with skills that transcend shortterm trends in computer hardware and software.
Independent Major in Computer Science
Students who wish to major in computer science do so by declaring an independent major. Students are encouraged to prepare a major course plan in consultation with their academic adviser in computer science. A typical course plan includes three introductory courses (110 or 205, 206 and 231), three core courses (240, 245 and one of 330, 340 or 345), six electives of a student’s choosing and a senior thesis. Students declare an independent major in the spring semester of their sophomore year. Such students should ensure that they have completed at least three courses in computer science by the end of their sophomore year (we highly recommend 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 110 or 205, 206, 231, any two of 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 of the sciences (biology, chemistry, geology, physics, psychology), classical and Near Eastern archaeology, economics, growth and structure of cities, mathematics, philosophy, and sociology, to learn computational methods and applications in their major area of study. The requirements for a minor in computational methods at Bryn Mawr are 110 or 205, 206, 231; one of 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 biology, chemistry, computer science, economics, geology, mathematics, physics, psychology and sociology).
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.
CMSC H101B Fluency with Information Technology
A study of the skills, concepts and capabilities involved in the design, implementation and effective use of information technology. Using a variety of quantitative techniques, we will explore a range of uses of information technology in various fields. (Dougherty)
CMSC B110 Introduction to Computing
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, Kumar, Division II and Quantitative Skills)
CMSC B120 Visualizing Information
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. (Xu, Quantitative Skills)
CMSC H130 Foundations of Rigorous Thinking
Develops rigorous thinking skills through the linguistic foundations of mathematics: logic and sets. Emphasis on using symbology to represent abstract objects and the application of formal reasoning to situations in computer science. (Lindell) Not offered in 200506.
CMSC H187B Computing Across the Sciences
This course covers the uses and internal workings of computational techniques used to study continuous and discrete systems in a variety of sciences. The first half covers numerical techniques for simulation and optimization, important in the analysis of continuous systems, and the second covers discrete systems emphasizing biological sequence alignment with DNA and proteins. No prior experience with programming is required. Prerequisite: One semester of calculus; one semester of any lab science is also highly recommended. (Wonnacott, Meneely, Division II)
CMSC H205A Introduction to Computer Science
A rigorous yearlong introduction to the fundamental concepts of computer science intended for students interested in doing more advanced work in technical and scientific fields. Includes the fundamental data structures of computer science and their algorithms. Examples and exercises will stress the mathematical aspects of the discipline, with a strong emphasis on programming and analytical problemsolving skills. Students without a strong (secondary school) mathematics or programming experience should take Computer Science 100 instead. (Wonnacott, Dougherty, Division II and Quantitative Skills)
CMSC B206 Introduction to Data Structures
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: Computer Science 205 or 110, or permission of instructor. (Xu, Blank, Dougherty, Wonnacott, Division II and Quantitative Skills)
CMSC H210A Linear Optimization and Game Theory
Covers in depth the mathematics of optimization problems with a finite number of variables subject to constraints. Applications of linear programming to the theory of matrix games and network flows are covered, as well as an introduction to nonlinear programming. Emphasis is on the structure of optimal solutions, algorithms to find them, and the underlying theory that explains both. (Butler, Division II and Quantitative Skills)
CMSC B212 Computer Graphics
Presents the fundamental principles of computer graphics: data structures for representing objects to be viewed, and algorithms for generating images from representations. Prerequisite: Mathematics 203 or 215, or permission of instructor. (Xu) Not offered in 200506.
CMSC H225A Fundamentals of Database Systems
An introduction to the principles of relational database design and use, including the entity/relationship data model and the logical algebra/calculus model behind query languages. An integrated laboratory component covers declarative programming using the international standard SQL. Prerequisites: Computer Science 206 and 231. (Lindell, Division II) Not offered in 200506.
CMSC B231 Discrete Mathematics
An introduction to discrete mathematics with strong applications to computer science. Topics include set theory, functions and relations, propositional logic, proof techniques, difference equations, graphs, and trees. (Weaver, Division II and Quantitative Skills; crosslisted as MATH B231 and PHIL B230)
CMSC H235A Information and Coding Theory
Covers the mathematical theory of the transmission (sending or storing) of information. Included are encoding and decoding techniques, both for the purposes of data compression and for the detection and correction of errors. (Lindell) Not offered in 200506.
CMSC B240 Computer Organization
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: Computer Science 206 or permission of instructor. (Kumar, Division II)
CMSC B245 Principles of Programming Languages
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 indepth treatment of mechanisms for sequence control, the runtime 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. (Wonnacott, Division II and Quantitative Skills)
CMSC B246 Programming Paradigms
Topics course; course content varies. Topic for 200506 is Programming in UNIX and C. Provides an indepth 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 Cspecific 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 mediumscaled development projects. Prerequisite: Computer Science 205a or 110. (Xu, Division II and Quantitative Skills)
CMSC B250 Computational Models in the Sciences
(Allen, Division II and Quantitative Skills; crosslisted as BIOL B250 and GEOL B250)
CMSC B325 Computational Linguistics
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)
CMSC B330 Algorithms: Design and Practice
This course examines the applications of algorithms to the accomplishments of various programming tasks. The focus will be on understanding of problemsolving methods, along with the construction of algorithms, rather than emphasizing formal proving methodologies. Topics include divide and conquer, approximations for NPComplete problems, data mining and parallel algorithms. Prerequisites: Computer Science 206 and 231. (Kumar, Division II and Quantitative Skills)
CMSC H340B Analysis of Algorithms
Qualitative and quantitative analysis of algorithms and their corresponding data structures from a precise mathematical point of view. Performance bounds, asymptotic and probabilistic analysis, worstcase and averagecase behavior. Correctness and complexity. Particular classes of algorithms such as sorting and searching are studied in detail. Prerequisites: Computer Science 206 and some additional mathematics at the 200 level, or permission of instructor. (Lindell) Not offered in 200506.
CMSC H345B Theory of Computation
Introduction to automata theory, formal languages and complexity. Introduction to the mathematical foundations of computer science: finite state automata, formal languages and grammars, Turing machines, computability, unsolvability and computational complexity. Prerequisites: Computer Science 206, and some additional mathematics at the 200 level, or permission of instructor. (Lindell)
CMSC B350 Compiler Design: Theory and Practice
An introduction to compiler and interpreter design, with emphasis on practical solutions, using compilerwriting tools in UNIX and the C programming language. Topics covered include lexical scanners, contextfree languages and pushdown automata, symbol table design, runtime memory allocation, machine language and optimization. (Wonnacott)
CMSC B355 Operating Systems
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)
CMSC B361 Emergence
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; crosslisted as BIOL B361)
CMSC B371 Cognitive Science
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. (Blank) Not offered in 200506.
CMSC B372 Artificial Intelligence
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; problemsolving; inference; natural language understanding; scene analysis; learning; decisionmaking. Topics are illustrated by programs from literature, programming projects in appropriate languages and building small robots. (Kumar, Division II and Quantitative Skills; crosslisted as PHIL B372) Not offered in 200506.
CMSC B380 Recent Advances in Computer Science
A topical course facilitating an indepth study on a current topic in computer science. Prerequisite: permission of instructor. (staff, Division II) Not offered in 200506.
CMSC H392A Advanced Topics: High Performance Scientific Computing
Prerequisite: permission of instructor. (Dougherty)
CMSC H394B Advanced Topics in Discrete Mathematics and Computer Science
(Lindell) Not offered in 200506.
CMSC B403 Supervised Work/Independent Study
