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Course Criteria
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3.00 Credits
Application of object-oriented techniques for systematic problem analysis and specification, design, coding, testing, and documentation. Semester-long project approach emphasizing larger programs. Managing program complexity using abstraction. Introduction to algorithm analysis and Big-O notation. Advanced language features. Basic sorting and searching algorithms. Recursion. Lecture two hours, technical activity and laboratory two hours. Prerequisite: CSC 15. Graded: Graded Student. Units: 3.0
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3.00 Credits
Notations for the specification of programming language syntax and semantics; attribute, translational, operational, axiomatic, algebraic, denotational, and action semantics. Applications of programming language syntax and programming language semantics. Use of meta languages to generate executable language definitions for language implementation, program transformation, program property analysis, and rapid software prototyping. Principles of logic, functional, and object-oriented programming languages. Prerequisite: Fully classified graduate status in Computer Science or Software Engineering. Graded: Graded Student. Units: 3.0
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3.00 Credits
Database management system (DBMS) architecture; database file organizations and access methods; the relational model and relational algebra; SQL query language; introduction to query optimization; concurrent transaction processing and backup and recovery; applications using embedded SQL, object types, and stored procedures; database analysis and design notations: EER, UML, and XML; web database environments; database security and administration throughout course. Note: Not intended for students who have completed CSC 174. Prerequisite: Fully classified graduate status in Computer Science or Software Engineering. Graded: Graded Student. Units: 3.0
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3.00 Credits
Overview of computer systems structure, covering hierarchical structure from software and hardware points of view. Concepts of relocation, linking, and loading; hardware-software interfaces from both application program and operating system points of view. Various CPU structures, including RISC and CISC machines, survey of tightly and loosely-coupled architecture, introduction to pipelined, distributed, and parallel systems, computer system communication principles including local and wide-area networks concepts, and various CAD tools and methodologies are introduced. Prerequisite: Fully classified graduate status in Computer Science, Software Engineering or Computer Engineering. Graded: Graded Student. Units: 3.0
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3.00 Credits
Design and analysis of algorithms. Classical design paradigms including greedy, divide-and-conquer, dynamic programming, and backtracking algorithmic methods. Alternative paradigms of computing including parallel and numerical approaches. Theoretical limits of computation. Selected additional topics such as genetic, approximation, and probabilistic algorithms. Prerequisite: Fully classified graduate status in Computer Science or Software Engineering. Graded: Graded Student. Units: 3.0
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1.00 Credits
Research methodology, problem formulation, and problem solving. Orientation to the requirements for Master's Thesis or Project. Presentations on various research topics. Prerequisite: Fully classified graduate status in Computer Science or Software Engineering, completion of at least 12 units of 200-level courses in Computer Science, and (GWAR Certification before Fall 09, or WPJ score of 70+, or at least a C- in ENGL 109M/W). Graded: Credit / No Credit. Units: 1.0
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3.00 Credits
The application of information technology and computer science to biological problems, in particular to biomedical science issues involving genetic sequences. Algorithms and their applications to DNA sequencing and protein database search; tools and techniques for data integration to transform genetic sequencing data into comprehensible information to study biological processes. Prerequisite: CSC 130, STAT 50, and graduate status; BIO 10 recommended. Graded: Graded Student. Units: 3.0
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3.00 Credits
Historical perspective of knowledge-based systems and their relationship to artificial intelligence. Concepts of knowledge representation and automated reasoning. Survey of expert systems in a variety of applications in engineering and other fields. Implementation of expert systems and expert system shells. Prerequisite: Fully classified graduate status in Computer Science or Software Engineering. Graded: Graded Student. Units: 3.0
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3.00 Credits
Nature of intelligence and possibility of its realization on digital computers via algorithmic and heuristic programming methods. Knowledge representation. Search procedures. Problem-solving paradigms and simulation of cognitive processes. Machine learning. Natural language understanding, expert systems, and knowledge engineering. Image understanding. Future of artificial intelligence and limits of machine intelligence. Prerequisite: Fully classified graduate status in Computer Science, Software Engineering or Computer Engineering. Graded: Graded Student. Units: 3.0
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3.00 Credits
Introduction to major paradigms and methods of machine learning. Inductive learning, explanation-based learning, classifier systems and genetic algorithms, analogical reasoning, case-based learning, connectionist learning, data driven approaches to empirical discovery, and basis of learning theory. Focus is on representative systems that have been built. Prerequisite: Fully classified graduate status in Computer Science, Software Engineering or Computer Engineering. Graded: Graded Student. Units: 3.0
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