Computer Science

• CAS CS 410: Advanced Software Systems
  Systems programming including such topics as project management, tools, I/O networking, multiprocessing, exception handling, and system services. Other topics are explored using C and Perl under the UNIX operating system. Requires a working knowledge of the C programming language and experience with UNIX as a user, or equivalent.
• CAS CS 411: Software Engineering
  Introduction to the construction of reliable software. Topics may include software tools, software testing methodologies, retrofitting, regression testing, structured design and structured programming, software characteristics and quality, complexity, entropy, deadlock, fault tolerance, formal proofs of program correctness, chief program teams, and structured walk-throughs. Effective Fall 2019, this course fulfills a single unit in each of the following BU Hub area: Teamwork/Collaboration.
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  • Teamwork/Collaboration
• CAS CS 440: Introduction to Artificial Intelligence
  Introduction to computer systems that exhibit intelligent behavior, in particular, perceptual and robotic systems. Topics include human computer interfaces, computer vision, robotics, game playing, pattern recognition, knowledge representation, planning.
• CAS CS 451: Distributed Systems
  Programming-centric introduction to how networks of computers are structured to operate as coherent single systems. Introducing principles of architecture, processes, communications, naming, synchronization, consistency and replication, fault tolerance and security, and paradigms such as web-based, object-based, file system, and consistency-based.
• CAS CS 454: Embedded Systems Development
  Lab-based course exploring concepts, techniques, best practices, and tools for the development of connected embedded systems, including: signal processing; sensing, control and actuation; programming and debugging on microprocessors; 1/0 interfacing and development of device drivers; and time-critical data handling.
• CAS CS 455: Computer Networks
  Concepts underlying the design of high-performance computer networks and scalable protocols. Topics include Internet design principles and methodology, TCP/IP implementation, packet switching and routing algorithms, multicast, quality of service considerations, error detection and correction, and performance evaluation.
• CAS CS 460: Introduction to Database Systems
  Introduction to database management systems. Examines entity-relationship, relational, and object-oriented data models; commercial query languages: SQL, relational algebra, relational calculus, and QBE; file organization, indexing and hashing, query optimization, transaction processing, concurrency control and recovery,integrity, and security.
• CAS CS 480: Introduction to Computer Graphics
  Introduction to computer graphics algorithms, programming methods, and applications. Focus on fundamentals of two- and three-dimensional raster graphics: scan-conversion, clipping, geometric transformations, and camera modeling. Introduces concepts in computational geometry, computer-human interfaces, animation, and visual realism. Effective Fall 2019, this course fulfills a single unit in the following BU Hub area: Digital/Multimedia Expression.
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  • Digital/Multimedia Expression
• CAS CS 491: Directed Study
  Independent study in Computer Science under the guidance of a faculty member. Student and supervising faculty member arrange and document expectations and requirements. Examples include internship opportunities for academic credit, in-depth study of a special topic, or independent research project.
• CAS CS 492: Directed Study
  Independent study in Computer Science under the guidance of a faculty member. Student and supervising faculty member arrange and document expectations and requirements. Examples include internship opportunities for academic credit, in-depth study of a special topic, or independent research project.
• CAS CS 504: Data Mechanics
  Examines how data moves and informs decisions within large systems. Applies mathematically rigorous tools and methods for data collection, retrieval, integration, and interpretation. Uses relational and MapReduce paradigms to assemble analysis, optimization, and decision-making algorithms to track and scale data.
• CAS CS 505: Introduction to Natural Language Processing
  Natural language processing (NLP) is a field of AI which aims to equip computers with the ability to intelligently process natural (human) language. This course explores statistical and machine learning techniques for the automatic analysis of natural language data.
• CAS CS 506: Computational Tools for Data Science
  Covers practical skills in working with data and introduces a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. Emphasizes hands-on application of methods via programming. Effective Fall 2019, this course fulfills a single unit in each of the following BU Hub areas: Research and Information Literacy, Teamwork/Collaboration.
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  • Research and Information Literacy
  • Teamwork/Collaboration
• CAS CS 507: Introduction to Optimization in Computing and Machine Learning
  Convex optimization algorithms and their applications to efficiently solving fundamental computational problems. Intended audience is advanced undergraduates and master students. Topics include modeling using mathematical programs, gradient descent algorithms, linear programming, Lagrangian duality, basics of complexity theory for optimization.
• CAS CS 511: Formal Methods 1
  Introduction to formal specification, analysis, and verification of computer system behavior. Topics include formal logical reasoning about computer programs and systems, automated and semi-automated verification, and algorithmic methodologies for ascertaining that a software system satisfies its formally specified properties. Cannot be taken for credit in addition to the course with the same number formerly entitled "Object-Oriented Software Principles."
• CAS CS 512: Formal Methods 2
  Introduction to formal specification, analysis, and verification of computer system behavior. Topics include formal logical reasoning about computer programs and systems, automated and semi-automated verification, and algorithmic methodologies for ascertaining that a computing system satisfies its formally specified properties.
• CAS CS 520: Programming Languages
  Concepts of programming languages: data, storage, control, and definition structures; concurrent and distributed programming; functional and logic programming.
• CAS CS 525: Compiler Design Theory
  Covers the basic mathematical theory underlying the design of compilers and other language processors and shows how to use that theory in practical design situations. Topics may include lexical analysis, parsing, syntax-directed translation, code optimization, and code generation.
• CAS CS 530: Advanced Algorithms
  Studies the design and efficiency of algorithms in several areas of computer science. Topics are chosen from graph algorithms, sorting and searching, NP-complete problems, pattern matching, parallel algorithms, and dynamic programming.
• CAS CS 531: Advanced Optimization Algorithms
  Optimization algorithms, highlighting the fruitful interactions between discrete and continuous. Intended audience is advanced master students and doctoral students. Topics include gradient descent algorithms, online optimization, linear and semidefinite programming, duality, network optimization, submodular optimization, approximation algorithms via continuous relaxations.

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