Computer Science (CS)

105L. Introduction to Computer Programming. (3)


108L. Computer Science for All: An Introduction to Computational Science and Modeling. (3)


151L. Computer Programming Fundamentals for Non-Majors. (3)


152L. Computer Programming Fundamentals. (3)


241L. Data Organization. (3)


251L. Intermediate Programming. (3)


259L. Data Structures with JAVA. (5)


261. Mathematical Foundations of Computer Science. (3)


293. Social and Ethical Issues in Computing. (1)


341L. Introduction to Computer Architecture and Organization. (3)


351L. Design of Large Programs. (4)


357L. Declarative Programming. (3)


361L. Data Structures and Algorithms. (3)


362. Data Structures and Algorithms II. (3)


365. Introduction to Scientific Modeling. (3)


*375. Introduction to Numerical Computing. (3)


390. Topics in Computer Science for Non-Majors-Undergraduate. (1-3, no limit Δ)


412. Introduction to Computer Graphics: Scanline Algorithms. (3)


422 / 522. Digital Image Processing. (3)


**423. Introduction to Complex Adaptive Systems. (3)


427 / 527. Principles of Artificially Intelligent Machines. (3)


429 / 529. Introduction to Machine Learning. (3)


442 / 542. Introduction to Parallel Processing. (3)


444 / 544. Introduction to Cybersecurity. (3)


456 / 556. Advanced Declarative Programming. (3)


**460. Software Engineering. (3)


464 / 564. Introduction to Database Management. (3)


467 / 567. Principles and Applications of Big Data. (3)


*471. Introduction to Scientific Computing. (3)


**481. Computer Operating Systems. (3)


**485. Introduction to Computer Networks. (3)


491. Special Topics-Undergraduates. (1-6 to a maximum of 12 Δ)


495 / 595. Advanced Topics in Computer Science. (3, no limit Δ)


499. Individual Study-Undergraduate. (1-3 to a maximum of 6 Δ)


500. Introduction to the Theory of Computation. (3)


506. Computational Geometry. (3)


510. Mobile Computing. (3)


512. Introduction to Computer Graphics. (3)


520. Topics in Interdisciplinary Biological and Biomedical Sciences. (3, no limit Δ)


521. Data Mining Techniques. (3)


522 / 422. Digital Image Processing. (3)


523. Complex Adaptive Systems. (3)


527 / 427. Principles of Artificially Intelligent Machines. (3)


529 / 429. Introduction to Machine Learning. (3)


530. Geometric and Probabilistic Methods in Computer Science. (3)


533. Experimental Methods in Computer Science. (3)


542 / 442. Introduction to Parallel Processing. (3)


544 / 444. Introduction to Cybersecurity. (3)


547. Neural Networks. (3)


550. Programming Languages and Systems. (3)


551. Individual Study-Graduate. (1-3 to a maximum of 6 Δ)


554 [554 / 454]. Compiler Construction. (3)


555. Advanced Topics in Compiler Construction. (3)


556 / 456. Advanced Declarative Programming. (3)


558. Software Foundations. (3)


561. Algorithms/Data Structure. (3)


564 / 464. Introduction to Database Management. (3)


565. Topics in Database Management. (3)


567 / 467. Principles and Applications of Big Data. (3)


575. Introductory Numerical Analysis: Numerical Linear Algebra. (3)


576. Introductory Numerical Analysis: Approximation and Differential Equations. (3)


580. The Specification of Software Systems. (3)


581. Fundamentals of Software Testing. (3)


583. Object Oriented Testing. (3)


585. Computer Networks. (3)


587. Advanced Operating Systems. (3)


590. Topics in Computer Science for Non-Majors-Graduate. (1-3, no limit Δ)


591. Special Topics-Graduate. (1-6, no limit Δ)


592. Colloquium. (1, may be repeated three times Δ)


595 / 495. Advanced Topics in Computer Science. (3, no limit Δ)


599. Master's Thesis. (1-6, no limit Δ)


600. Computer Science Research Practicum. (3)


650. Reading and Research. (3, may be repeated once Δ)


691. Seminar in Computer Science. (1-6 to a maximum of 12 Δ)


699. Dissertation. (3-12, no limit Δ)


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MSC11 6325
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Phone: (505) 277-8900
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