StudyQA — Master: Computer Science — University of New Mexico (Page 48)

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Description Contents Requirements Funding

The Department of Computer Science of the University of New Mexico has offered the Master of Science in Computer Science degree since 1973. The purpose of the master’s degree program is to prepare students to work as professionals in the computer science field.

The Master of Science in computer science has two tracks, each having a separate set of requirements. Both tracks are offered under Plan I (thesis option) and Plan II (course work only option).

The Master of Science in Computer Science (M.S.) can be completed under Plan I or Plan III. A brochure describing the program and requirements can be obtained from the department.

Plan I

In addition to all Graduate Studies requirements for the master’s degree, the department also requires the following:

32 credit hours of approved graduate courses.
At least 2 credit hours of CS 592 (Colloquium), taken at UNM.
At least 26 of the 32 credit hours must be in courses offered by the Computer Science department at the 500-level or above.
Completion of a minimum of two courses from each category below with a grade of B- or better:
- a. Mathematical Methods – CS 500, 530, 550, 558, 561.
- b. Empirical Methods – CS 512, 522, 523, 527, 529, 547.
- c. Engineering/System Building Methods – CS 554, 580, 585, 587.
Passing the master’s examination. For Plan I students, the master’s examination is the defense of thesis.

Plan III

In addition to all Graduate Studies requirements for the master’s degree, the department also requires the following:

32 credit hours of approved graduate courses.
At least 2 credit hours of CS 592 (Colloquium), taken at UNM.
In addition to Colloquium, at least 24 of the 32 credit hours must be in courses offered by the Computer Science Department at the 500-level or above.
Completion of a minimum of two courses from each category below with a grade of B- or better:
- a. Mathematical Methods – CS 500, 530, 550, 558, 561.
- b. Empirical Methods – CS 512, 522, 523, 527, 529, 547.
- c. Engineering/System Building Methods – CS 554, 580, 585, 587.

Courses

CS 341L. Introduction to Computer Architecture and Organization. (3)
CS 351L. Design of Large Programs. (4)
CS 357L. Declarative Programming. (3)
CS 361L. Data Structures and Algorithms. (3)
CS 362. Data Structures and Algorithms II. (3)
CS 365. Introduction to Scientific Modeling. (3)
CS *375. Introduction to Numerical Computing. (3)
CS 390. Topics in Computer Science for Non-Majors-Undergraduate. (1-3, no limit Δ)
CS 412. Introduction to Computer Graphics: Scanline Algorithms. (3)
CS 413. Introduction to Ray and Vector Graphics. (3)
CS 422 / 522. Digital Image Processing. (3)
CS **423. Introduction to Complex Adaptive Systems. (3)
CS 427 / 527. Principles of Artificially Intelligent Machines. (3
CS 429 / 529. Introduction to Machine Learning. (3)
CS 442 / 542 [*442]. Introduction to Parallel Processing. (3)
CS 444 / 544. Introduction to Cybersecurity. (3)
CS 454 / 554. Compiler Construction. (3
CS 456 / 556. Advanced Declarative Programming. (3)
CS **460. Software Engineering. (3)
CS 464 / 564. Introduction to Database Management. (3)
CS 467 / 567. Principles and Applications of Big Data. (3
CS *471. Introduction to Scientific Computing. (3)
CS **481. Computer Operating Systems. (3)
CS **485. Introduction to Computer Networks. (3)
CS 491. Special Topics-Undergraduates. (1-6 to a maximum of 12 Δ
CS 495 / 595. Advanced Topics in Computer Science. (3, no limit Δ)
CS 499. Individual Study-Undergraduate. (1-3 to a maximum of 6 Δ)
CS 500. Introduction to the Theory of Computation. (3)
CS 506. Computational Geometry. (3
CS 510. Mobile Computing. (3)
CS 512. Introduction to Computer Graphics [Advanced Image Synthesis]. (3)
CS 520. Topics in Interdisciplinary Biological and Biomedical Sciences. (3, unlimited Δ)
CS 521. Data Mining Techniques. (3
CS 522 / 422. Digital Image Processing. (3)
CS 523. Complex Adaptive Systems. (3)
CS 527 / 427. Principles of Artificially Intelligent Machines. (3)
CS 529 / 429. Introduction to Machine Learning. (3
CS 530. Geometric and Probabilistic Methods in Computer Science. (3)
CS 531. Machine Learning [Pattern Recognition]. (3)
CS 532. Computer Vision. (3)
CS 533. Experimental Methods in Computer Science. (3
CS 542 / 442. Introduction to Parallel Processing. (3)
CS 544 / 444. Introduction to Cybersecurity. (3)
CS 547. Neural Networks. (3)
CS 550. Programming Languages and Systems. (3
CS 551. Individual Study-Graduate. (1-3 to a maximum of 6 Δ)
CS 554 / 454. Compiler Construction. (3)
CS 555. Advanced Topics in Compiler Construction. (3
CS 556 / 456. Advanced Declarative Programming. (3)
CS 558. Software Foundations. (3)
CS 561. Algorithms/Data Structure. (3)
CS 564 / 464. Introduction to Database Management. (3)
CS 565. Topics in Database Management. (3)
CS 567 / 467. Principles and Applications of Big Data. (3)
CS 575. Introductory Numerical Analysis: Numerical Linear Algebra. (3
CS 576. Introductory Numerical Analysis: Approximation and Differential Equations. (3)
CS 580. The Specification of Software Systems. (3)
CS 581. Fundamentals of Software Testing. (3
CS 583. Object-Oriented Testing. (3)
CS 585. Computer Networks. (3)
CS 587. Advanced Operating Systems. (3)
CS 590. Topics in Computer Science for Non-Majors-Graduate. (1-3, no limit Δ)
CS 591. Special Topics-Graduate. (1-6, no limit Δ)
CS 592. Colloquium. (1 to a maximum of 4 Δ)
CS 595 / 495. Advanced Topics in Computer Science. (3, no limit Δ)
CS 599. Master's Thesis. (1-6, no limit Δ)

Completed international admissions application
Must hold a bachelor's degree from an accredited college or university in the United States or its equivalent in another country.
Generally, applicants must have a cumulative grade point average of at least 3.0 (B) or its equivalent in their last two undergraduate years and in their major field.
$70.00 application fee
Official English language proficiency results from either the TOEFL, IELTS, PTE-Academic, or Cambridge CPE or CAE
Complete transcripts and degree certificates with official English translations, if necessary, from all post-secondary schools attended must be uploaded to the graduate application
Official academic transcripts and degree certificates and with official English translations, if necessary, from all post-secondary institutions attended
Financial Responsibility form and official bank statements