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• CSC 391: Computer Science Internship II

  3.00 Credits

  Shippensburg University of Pennsylvania

  
  Contact the department for further information on internships.
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• CSC 392: Computer Science Internship III

  3.00 Credits

  Shippensburg University of Pennsylvania

  
  Contact the department for further information on internships.
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• CSC 393: Selected Topics in Computer Science

  4.00 Credits

  Shippensburg University of Pennsylvania

  
  Opportunity to offer courses of departmental major interest not covered by the regular courses. Prerequisite: Junior major status or permission of instructor.
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• CSC 399: Independent Study in Computer Science

  3.00 Credits

  Shippensburg University of Pennsylvania

  
  Independent study affords students the opportunity to engage in independent study related to their major field, a supporting area, or specialized interest.
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• CSC 402: Big Data Analytics

  4.00 Credits

  Shippensburg University of Pennsylvania

  
  We are living in data-intensive world. Efficiently extracting, interpreting, and learning from very large datasets requires efficient and scalable algorithms as well as new data management technologies. Machine learning techniques and high performance computing make the efficient analysis of large volumes of data. In this course we explore big dataset analysis techniques and apply it to the distributed. This course is highly interactive. Students are expected to make use of technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges. Prerequisite:    CSC 310 FOR LEVEL U WITH MIN. GRADE OF C AND MAT 217 FOR LEVEL U WITH MIN. GRADE OF D
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• CSC 403: Machine Learning

  4.00 Credits

  Shippensburg University of Pennsylvania

  
  This introductory course gives an overview of machine learning. This is a wide ranging field including topics such as: classification, linear regression, Principal Component Analysis (PCA), neural networks, bagging and boosting, support vector machines, hidden Markov models, Bayesian networks, Q-learning, reinforcement learning. Prerequisite:    (MAT 117A FOR LEVEL U WITH MIN. GRADE OF D OR MAT 117B FOR LEVEL U WITH MIN. GRADE OF D OR MAT 217 FOR LEVEL U WITH MIN. GRADE OF D OR MAT 375 FOR LEVEL U WITH MIN. GRADE OF D) AND CSC 310 FOR LEVEL U WITH MIN. GRADE OF D
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• CSC 410: Theoretical Foundations of Computer Science

  3.00 Credits

  Shippensburg University of Pennsylvania

  
  Topics include finite automata, regular languages, regular expressions, and regular grammars; pushdown automata and context-free languages; Turing machines; Church-Turing Thesis; the Halting Problem; undecidability; classes of languages, including the Chomsky hierarchy and the classes P, NP, and NP-Complete. Proof techniques for showing language (non)membership in a class. This course is not available for graduate credit. Prerequisite:    CSC 310 FOR LEVEL U WITH MIN. GRADE OF C
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• CSC 431: Computer Networks

  4.00 Credits

  Shippensburg University of Pennsylvania

  
  Studies protocol suites, emphasizing the TCP/IP 4-layer model. Topics included are network addresses, sub netting, client/server network programming via the sockets API, network utilities, architecture of packets, routing, fragmentation, connection and termination, connection-less applications, data flow, and an examination of necessary protocols at the link layer, particularly Ethernet. Other topics may include FDDI, wireless, ATM, congestion control, and network security. This class is available for graduate credit. Prerequisite:    CMPE 220 FOR LEVEL U WITH MIN. GRADE OF C OR SWE 200 FOR LEVEL U WITH MIN. GRADE OF C OR GPRE FOR MIN. SCORE OF 1
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• CSC 462: Artificial Intelligence

  4.00 Credits

  Shippensburg University of Pennsylvania

  
  Overview of artificial intelligence. Emphasis on basic tools of AI, search and knowledge representation, and their application to a variety of AI problems. Search methods include depth-first, breadth-first, and AI algorithms; knowledge representation schemes include propositional and predicate logics, semantic nets and frames, and scripts. Planning using a STRIPS-like planner will also be addressed. Areas that may be addressed include natural language processing, computer vision, robotics, expert systems, and machine learning. This class is available for graduate credit. Prerequisite:    CSC 211 FOR LEVEL U WITH MIN. GRADE OF C OR SWE 200 FOR LEVEL U WITH MIN. GRADE OF C OR GPRE FOR MIN. SCORE OF 1
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• CSC 471: Database Management Systems

  3.00 Credits

  Shippensburg University of Pennsylvania

  
  Detailed examination of theory and practical issues underlying the design, development, and use of a DBMS. Topics include characteristics of a well-designed database; high-level representation of an application using ER modeling; functional dependency theory, normalization, and their application toward a well-designed database; abstract query languages; query languages; concurrency; integrity; security. Advanced topics may be included (e.g., distributed databases; object-oriented databases). Theory to practice is applied in a number of projects involving the design, creation, and use of a database. This class is not available for graduate credit. Prerequisite:    CSC 211 FOR LEVEL U WITH MIN. GRADE OF C OR SWE 200 FOR LEVEL U WITH MIN. GRADE OF C OR GPRE FOR MIN. SCORE OF 1
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