Computer Science

View courses in

  • CAS CS 440: Introduction to Artificial Intelligence
    Undergraduate Prerequisites: CAS CS 112; and CASCS132 or CASMA242, or consent of instructor.
    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
    Undergraduate Prerequisites: CAS CS 112 and CAS CS 210.
    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 455: Computer Networks
    Undergraduate Prerequisites: CAS CS 112 and CAS CS 210; CAS CS 350 is recommended; or consent of instructor.
    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
    Undergraduate Prerequisites: CAS CS 112; or equivalent.
    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
    Undergraduate Prerequisites: CAS CS 112; and CASCS132 or CASMA242.
    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.
  • CAS CS 491: Directed Study
    Undergraduate Prerequisites: consent of instructor and approval of the Directed Study Committee (CAS Room 105).
  • CAS CS 492: Directed Study
    Undergraduate Prerequisites: consent of instructor and approval of the Directed Study Committee (CAS Room 105).
  • CAS CS 511: Object-Oriented Software Principles
    Undergraduate Prerequisites: CAS CS 320 and CAS CS 411; or consent of instructor.
    Specification, programming, and analysis of large-scale, reliable, and reusable Java software using object-oriented design principles. Topics may include object-oriented programming, object models, memory models, inheritance, exceptions, namespaces, data abstraction, design against failure, design patterns, reasoning about objects.
  • CAS CS 512: Formal Methods for High-Assurance System Design and Analysis
    Undergraduate Prerequisites: CAS CS 320 or CAS CS 330 or CAS CS 350; or consent of instructor.
    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
    Undergraduate Prerequisites: CAS CS 320; or consent of instructor.
    Graduate Prerequisites: CAS CS 320 or CAS CS 332; or consent of instructor.
    Concepts of programming languages: data, storage, control, and definition structures; concurrent and distributed programming; functional and logic programming.
  • CAS CS 530: Advanced Algorithms
    Undergraduate Prerequisites: CAS CS 330; or consent of instructor.
    Graduate Prerequisites: CAS CS 330; or consent of instructor.
    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 535: Complexity Theory
    Undergraduate Prerequisites: CAS CS 332; or consent of instructor.
    Graduate Prerequisites: CAS CS 332.
    Covers topics of current interest in the theory of computation chosen from computational models, games and hierarchies of problems, abstract complexity theory, informational complexity theory, time-space trade-offs, probabilistic computation, and recent work on particular combinatorial problems.
  • CAS CS 538: Fundamentals of Cryptography
    Undergraduate Prerequisites: CAS CS 131 and CAS CS 237; or consent of instructor.
    Graduate Prerequisites: CAS CS 332.
    Basic Algorithms to guarantee confidentiality and authenticity of data. Definitions and proofs of security for practical constructions. Topics include perfectly secure encryption, pseudorandom generators, RSA and Elgamal encryption, Diffie-Hellman key agreement, RSA signatures, secret sharing, block and stream ciphers.
  • CAS CS 542: Machine Learning
    Undergraduate Prerequisites: CAS CS 112; or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.
    Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.
  • CAS CS 548: Advanced Cryptography
    Undergraduate Prerequisites: CAS CS 538; or consent of instructor.
    Continuation of CAS CS 538. Advanced techniques to preserve confidentiality and authenticity against active attacks, zero-knowledge proofs; Fiat-Shamir signature schemes; non-malleable public-key encryption; authenticated symmetric encryption; secure multiparty protocols for tasks ranging from Byzantine agreement to mental poker to threshold cryptography.
  • CAS CS 552: Introduction to Operating Systems
    Undergraduate Prerequisites: CAS CS 112 and CAS CS 210; and competency with C/C++. CASCS350 is recommended, or consent of instructor.
    Examines process synchronization; I/O techniques, buffering, file systems; processor scheduling; memory management; virtual memory; job scheduling, resource allocation; system modeling; and performance measurement and evaluation.
  • CAS CS 556: Advanced Computer Networks
    Undergraduate Prerequisites: CAS CS 455; or consent of instructor.
    Strengthens understanding of networking issues and solutions. Relates fundamental concepts, requirements, and design tradeoffs to scheduling, congestion control, routing, traffic management, wireless access and mobility, and applications. Considers how networking may evolve to provide ubiquitous support for quality-of-service in heterogeneous environments.
  • CAS CS 558: Computer Networks Security
    Undergraduate Prerequisites: CAS CS 210; , CASCS237 is also recommended; or consent of instructor.
    Introduces basic principles and techniques of building secure information systems. Covers network security, web security, privacy, and basic cryptographic tools (symmetric and public key cryptography, encryption, key exchange, hashing and signatures). Broader social, legal and political aspects of security addressed.
  • CAS CS 562: Advanced Database Applications
    Undergraduate Prerequisites: CAS CS 460; or consent of instructor.
    Research issues in the design and implementation of modern database systems. Spatial, temporal, and spatiotemporal index structures. Indexing methods for image and multimedia databases and data warehouses. New data analysis techniques for large databases, clustering and rule discovery for very large datasets.
  • CAS CS 565: Data Mining
    Undergraduate Prerequisites: CAS CS 112; or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.
    Introduction to data mining concepts and techniques. Topics include association and correlation discovery, classification and clustering of large datasets, outlier detection. Emphasis on the algorithmic aspects as well as the application of mining in real-world problems.