Catalogue

Computer Science

cs.bard.edu

Faculty

Sven Anderson (director), Valerie Barr, Robert W. McGrail, Kerri-Ann Norton, Keith O’Hara, Rose Sloan

Overview

Computing is an integral part of contemporary life. Computer science encompasses the study of computing technology, theory, and applications in all contexts, including mobile computing, modeling, robotics and autonomous vehicles, and the internet. The Computer Science Program at Bard offers courses tailored to the interests of students from across the College. The program focuses on the fundamental ideas of computer science and introduces students to multiple programming languages and paradigms. It covers theoretical, applied, and systems-oriented topics. Most courses include hands-on projects so that students can learn by building, and by participating in research projects in laboratories devoted to cognition, computational biology, robotics, and symbolic computation.

The curriculum is designed to offer many opportunities for students whose interest in computing arises from their own disciplinary context. Computer science has links with, among other programs and concentrations, Mind, Brain, and Behavior, Experimental Humanities, Mathematics, Film and Electronic Arts, and Music, and students from these fields often use their computing skills and knowledge in carrying out Senior Projects.

Requirements

Before Moderation, a student in the Computer Science Program should complete or be enrolled in Computer Science 141, 145, and 201, as well as Mathematics 141 (or the equivalents). Students are expected to follow standard divisional procedures for Moderation and to fulfill the college-wide distribution and First-Year Seminar requirements. To graduate, a student in the program must take Computer Science 301, 305, a statistics course such as 275; one systems course such as 226, 326, 327; at least two other computer science courses, one numbered above 201 and the other numbered 300 or above; and complete a Senior Project.

Recent Senior Projects

• “Carbon Footprint of Machine-Learning Algorithms” 
• “A Computational Method for the Image Segmentation of Pigmented Skin Lesions” 
• “The Development of a Collaborative Tool to Teach Debugging”
• “Testing and Improving an Optimization-Based Digital Colorblindness Corrective Filter”

Facilities

Program facilities at the Gabrielle H. Reem and Herbert J. Kayden Center for Science and Computation include computer and hardware teaching labs, a cognitive systems lab, robotics lab, dedicated computer server room, and study space with wireless networking.

Courses

The following core courses are offered every year: Computer Science 141/143, Object-Oriented Programming; Computer Science 145, Discrete Mathematics; Computer Science 201, Data Structures; Computer Science 275, Statistics for Computing; Computer Science 301, Algorithms; Computer Science 305, Design of Programming Languages; and Computer Science 326, Operating Systems. Elective courses are offered at least once every three years or by tutorial; recent examples include Databases, Mobile Applications and Interfaces, The Computational Image, Bioinformatics, and Machine Learning.

Introduction to Computing: Simulating Reality
Computer Science 115
How do rumors and fashions spread in society? Does a small change in environmental temperature disrupt an ecosystem? Questions like these are explored, using computers to create virtual worlds. This introduction to modeling and -simulation is for students who are interested in creating computer models of objects, processes, and complex systems using computer software. Prerequisites: a strong background in precalculus mathematics or the equivalent, and a passing score on Part I of the Mathematics Diagnostic

Introduction to Computing: Web Informatics
Computer Science 116
An introduction to content deployment for the World Wide Web. Participants construct social networking software, similar in scope to blogs or Facebook, using a dynamic web programming system. Strong emphasis is placed on the development of flexible applications that efficiently store and process data and metadata. In addition to basic computer programming, various XML technologies are employed. Prerequisite: passing score on Part I of the Mathematics Diagnostic.

Foundations of Mind, Brain, and Behavior
Computer Science 131
CROSS-LISTED: MBB, 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 is taught by faculty from biology, computer science, linguistics, philosophy, and psychology, who combine their different approaches to explore how humans and other intelligent systems feel, perceive, reason, plan, and act. Prerequisite: precalculus or its equivalent.

Object-Oriented Programming
Computer Science 141
CROSS-LISTED: EXPERIMENTAL HUMANITIES, MBB
An introduction to the methodologies of object-oriented design and programming, which are used throughout the Computer Science curriculum. Students learn how to move from informal problem statement to increasingly precise problem specifications and the design and implementation of a solution. Problems drawn from areas such as graphics, animation, and simulation. 

Object-Oriented Programming with Robots
Computer Science 143
CROSS-LISTED: MBB
Students learn how to move from an informal problem statement to the design and implementation of a solution. Good programming habits are emphasized.

Discrete Mathematics
Computer Science 145
This course emphasizes creative problem solving, linking language to logic, and learning  to read and write proofs. Topics include propositional logic, predicate logic, inductive proof, sets, relations, functions, introductory combinatorics, and discrete probability. Prerequisite: Mathematics 141 or programming experience.

Data Structures
Computer Science 201
CROSS-LISTED: MBB
This course covers the 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, students focus on the construction of more sophisticated computer programs that employ the most important and useful data structures, including files, lists, stacks, trees, and graphs. Prerequisite: Computer Science 143 or permission of the instructor.

Programming Nature
Computer Science 210
Students in the course, who should have prior programming experience, develop the necessary tools for modeling biological and physical objects that can move, interact, divide, and evolve, with a specific application of simulating biological cells. They learn how to pose a question about a natural system, develop a set of rules, and implement simulations to formulate predictions about the dynamics of that system. Prerequisite: Computer Science 143 or permission of the instructor.

Principles of Computing Systems
Computer Science 226
CROSS-LISTED: EXPERIMENTAL HUMANITIES
As programs scale up from a single author, user, and computer to those designed, written, maintained, and used by multiple people that run on many computers, considerations beyond algorithms alone are magnified. This course takes a systems perspective to the study of computers. Students explore computers from the ground up, using a variety of programming languages and tools, such as the command line, debuggers, and version control. Prerequisite: Computer Science 141 or permission of the instructor.

Introduction to Automated Deduction
Computer Science 230
Topics covered include propositional, first-order, and second-order logic; satisfiability and term unification; logical resolution; and Horn clause programming. Term rewriting systems, lambda calculus and type theory, and general automated theorem proving may also be addressed. Prerequisite: Computer Science 145 or the equivalent.

Introduction to Artificial Intelligence
Computer Science 251
CROSS-LISTED: MBB
The course emphasizes elements of artificial intelligence that are compatible with biologically based intelligence (e.g., neural computation). Techniques explored include automated reasoning, machine learning, evolutionary learning, heuristic search, and behavior-based robot control. Application examples are drawn from robotics, game play, logic, visual perception, and natural language processing.

Algorithms
Computer Science 301
The focus of this course is on the design and analysis of correct and efficient computer algorithms. Topics include sorting, divide-and-conquer algorithms, greedy algorithms, dynamic programming algorithms, and graph algorithms. Advanced topics in algorithms may be selected from specialized areas of the mathematical and empirical sciences. Prerequisites: Computer Science 145 and 201, or Mathematics 261.

Design of Programming Languages
Computer Science 305
Students explore important issues in the design of programming languages, including type systems, ­procedure activation, parameter passing, data encapsulation, dynamic memory allocation, and concurrency. In addition, the functional, logical, and object-oriented programming paradigms are presented. Prerequisite: Computer Science 201.

Theory of Computation
Computer Science 312
This course introduces several computational models developed to formalize the notion of an algorithm. It also offers detailed discussion of primary topics in the theory of computation, including the theory of recursive functions, Turing machines, and several undecidable problems, such as the halting problem. Prerequisite: Computer Science 145 or Mathematics 261.

The Computational Image
Computer Science 317
Using algorithms and approaches from computational geometry, computer graphics, image processing, computer vision, and augmented reality, students build computer systems that are visually interactive. Topics include image formation, feature extraction, object segmentation, recognition and tracking, rendering, and multiview geometry.

Bioinformatics and Beyond
Computer Science 320
The course, for students with prior object-oriented programming experience, introduces the basics of bioinformatics and biological statistical analysis. Students develop the necessary tools for analyzing and aligning biological sequences, building phylogenetic trees, and using statistical tests. By the end of the semester, they will have learned how to develop a hypothesis, test their hypothesis, and statistically analyze their data. Prerequisites: Computer Science 275 and Biology 244, or permission of the instructor.

Databases: Theory and Practice
Computer Science 321
An introduction to the design, implementation, and uses of databases. Topics include database design, database models, integrity, concurrency, security, and database query languages. Prerequisite: a 100-level Computer Science course.

Operating Systems
Computer Science 326
The operating system provides the interface that makes it possible for users and their programs to interact with computer hardware. Whether on a mainframe or mobile phone, the operating system makes it possible for a computer to be shared by many users and processes in a secure manner. This course examines the major components of modern operating systems, with an emphasis on how processes and memory are managed for efficiency and security. Prerequisite: Computer Science 201.

Distributed Systems
Computer Science 327
Computing systems are increasingly built in a networked, distributed manner. These systems, often spanning the globe, coordinate to solve problems involving massive amounts of data in a highly fault-tolerant fashion. Students examine the challenges of extending traditional computing systems concepts (e.g. shared state, hardware abstraction, scheduling, file systems, timing, authentication) to a networked setting as well as the architectures, algorithms, and technologies used to design, build, and evaluate distributed computer systems. Prerequisite: Computer Science 201 or permission of the instructor.

Constraint Programming
Computer Science 329
An introduction to constraint programming (CP), a declarative programming paradigm in which a program is a set of constraints and a process is the search for a solution to a constraint set. Topics necessary to form a foundational understanding of CHR (constraint handling rules) include multisets, term rewriting, termination, confluence, operational and declarative semantics, computational complexity, and NP-completeness. The course involves significant programming and the writing of mathematical proofs. Prerequisite: Computer Science 201 or Mathematics 261.

Games Systems: Platforms, Programs, and Power
Computer Science 336
CROSS-LISTED: EXPERIMENTAL HUMANITIES
This course explores the design and implementation of historic and modern computing systems for games, including hardware, software, and their interface. Beyond creating games, students critically reflect on how the architectural and programming choices in games can encode inequality and particular worldviews procedurally, as much as visuals, audio, and narrative. Also covered: graphics programming, networking, and peripherals; mid-level concerns such as software engineering, design patterns, concurrency, and interfaces; and issues related to emulation, ethics, platform studies, and media archaeology. Prerequisite: Computer Science 201.

Machine Learning
Computer Science 352
CROSS-LISTED: MBB
Machine learning is a field in which algorithms learn to improve themselves based on their interactions with an environment. This course investigates a broad array of techniques from machine learning and statistical pattern recognition. Topics addressed include unsupervised learning, clustering, dimensionality reduction, supervised learning, neural networks, reinforcement learning, and learning theory. Applications are drawn from computer vision, speech recognition, autonomous navigation, natural language processing, and data mining. Prerequisites: Mathematics 142 and Computer Science 201.

Intelligent Robotics and Perception
Computer Science 360
An overview of topics in computational perception, machine learning, and robotics. Students learn the underlying principles and methods of intelligent robotic systems, including techniques from sensor processing; robot software architecture; and supervised, unsupervised, and reinforcement learning. Throughout the semester, students collaborate to build an intelligent robotic system.