Computer Science

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The training of M.S. graduates in computer science should provide them with the knowledge and skills to hold professional positions in the development and design of computer systems, and in the design and implementation of new software applications; to hold administrative positions that require planning and evaluation of computer-based systems; to teach in computer science; and to be prepared for further study and research at the doctorate level.

Because of the rapid rate of change in the field, students must be well grounded in the basic aspects of computer science and be capable of learning new ideas by following the research and professional literature, and by adapting independently to changes in approaches, languages, and system. Furthermore, they must have experience with computer projects of a realistic scale so as to develop confidence in their ability to think and work independently.

Most of the graduate classes start in mid-afternoon, or later, and some begin in the evening. The exceptions are advanced research seminars. Students who have a job can get a Master's degree in our Program provided they can get some time off in the late afternoon one or two days a week. Probably it will not be possible for a student to obtain a Master's degree by taking classes only after 6PM and fairly unlikely if only taking classes that start at 4:30 or later.

Upon entering the M.S. program, each student will be assigned a faculty advisor, and, with the help of the advisor, will choose an overall study plan which should satisfy the various requirements as well as plan for going more deeply into some area of particular interest to the student. It is important that the student continue to consult with that advisor before registering each semester. (Students should not simply rely on the advice and opinions of other students. Although this input is important, the information is often inaccurate, might be outdated, and students should not rely solely upon it.) Students need not choose immediately between the Master's essay and the Master's thesis options, but should talk about the possibilities with their advisor. Students may change advisors as they become more familiar with the program and clearer about what area they wish to specialize in, and in particular if the Master's thesis option is chosen.

In order to ensure that the students have good preparation in several of the basic fields of computer science, the department has specified two categories of courses (Category A & Category B). Within each category, the courses are divided into “advisory” three levels: 

- Basic level: These are courses designed to help MS students filling in any gaps in their computer science background

- Core level: These are courses that are most suitable to the MS-level of study. 

- Advanced level: These are advanced courses, which typically require pre-requisites from the core-level courses. MS students are advised to take these courses only if they are in their area of interest and have finished all the pre-requisites.

This split is mainly advisory to help MS find appropriate courses. 

Here is a list of all courses in each category (Classification of A and B is for M.Sc. degree only, not applicable to Ph.D. students) : 

Category A

  • Basic: 508, 512
  • Core: 509,510,513, 529
  • Advanced: 514,521,522,524, 527,538,540,556, relevant 67x courses

Category B

  • Basic: 518, 537, 544
  • Core: 505, 515,519,520,523, 530, 534,535, 532, 539,  552
  • Advanced: 507, 516, 533, 536,541, 545, 546, 547, 553, relevant 67x courses

Note that this classification of courses is not set in concrete. The Graduate Committee may add and remove courses from this list, or change the placement of a course in this partition, as it deems necessary (for example, to respond to changes in course content or scheduling, or to incorporate new course offerings). Such changes will be posted in a timely fashion on both physical and electronic graduate student ``bulletin boards.''


01:198:324 Numerical Methods*    
01:198:336 Principles of Information and Data Management*   Fall, Spring, Summer
01:198:415 Compilers*   Spring
01:198:424 Modeling and Simulation of Continuous Systems*    
01:198:425 Computer Methods in Statistics*    
01:198:428 Introduction to Computer Graphics*    
01:198:431 Software Engineering*   Fall
01:198:440 Introduction to Artificial Intelligence*   Fall
01:198:442 Topics in Computer Science*    
01:198:443 Topics in Computer Science*    
01:198:444 Topics in Computer Science*    
16:198:500 Proseminar in Computer Science   Fall, Spring
16:198:503 Computational Thinking   Fall
16:198:504 Computational Modeling   Fall
16:198:505 Computer Structures B Spring
16:198:507 Advanced Computer Architecture B Fall
16:198:508 Formal Language and Automata   Fall
16:198:509 Foundations of Computer Science A Fall
16:198:510 Numerical Analysis A Fall, Spring
16:198:512 Introduction to Data Structures and Algorithms   Fall, Spring
16:198:513 Design and Analysis of Data Structures and Algorithms A Fall, Spring
16:198:514 Design And Analysis Of Data Structures And Algorithms II A Spring
16:198:515 Programming Languages And Compilers I B Fall
16:198:516 Programming Languages And Compilers II B Spring
16:198:518 Operating Systems Design   Fall, Spring
16:198:519 Operating System Theory B Fall
16:198:520 Introduction To Artificial Intelligence B Fall, Spring
16:198:521 Linear Programming A Fall
16:198:522 Network and Combinatorial Optimization Algorithms A Spring
16:198:523 Computer Graphics B Spring
16:198:527 Computer Methods For Partial Differential Equations A Spring
16:198:529 Computational Geometry A Fall
16:198:530 Principles of Aritificial Intelligence B Fall
16:198:532 Foundations Of Knowledge Representation B Spring
16:198:533 Natural Language Processing B Spring
16:198:534 Computer Vision B Spring
16:198:535 Pattern Recognition: Theory & Applications B Spring
16:198:536 Machine Learning B Spring
16:198:538 Complexity Of Computation A Spring
16:198:539 Database Systems Implementation   Fall
16:198:540 Combinatorial Methods In Complexity Theory A Spring
16:198:541 Database Systems B Spring
16:198:544 Computer Security   Spring
16:198:545 Distributed Systems B Spring
16:198:546 Computer System Security   Fall
16:198:547 The Security and Dependability of Distributed Systems B Spring
16:198:552 Computer Networks B Fall
16:198:553 Design of Internet Services B Spring
16:198:580 Topics In Computers In Biomedicine B Fall
16:198:583 Topics In Software Design B Fall
16:198:596 Topics In The Foundations Of Computer Science A  
16:198:598 Topics In Artificial Intelligence B  
16:198:601 Selected Problems In Computer Science    
16:198:602 Selected Problems In Computer Science    
16:198:603 Selected Problems In Computer Science    
16:198:604 Selected Problems In Computer Science    
16:198:605 Selected Problems In Computer Science    
16:198:606 Selected Problems In Computer Science    
16:198:607 Problems In Numerical Methods    
16:198:608 Problems In Numerical Methods    
16:198:671 Seminar in Computer Science   Fall, Spring
16:198:672 Seminar in Computer Science   Fall
16:198:673 Seminar in Computer Science    
16:198:674 Seminar in Computer Science    
16:198:701 Research In Computer Science   Fall
16:198:702 Research In Computer Science   Spring
16:198:800 Matriculation Continued   Fall, Spring

In addition to the general admission criteria of the Graduate School, the department requires that applicants to the M.S. program have completed an accredited undergraduate program in Computer Science, or at least taken the core prerequisite courses for the undergraduate degree as listed below:

  1. A substantial background in mathematics, especially in calculus (as in 640:151-152), linear algebra (as in 640:250), finite mathematics (as in 198:205), probability/combinatorics (as in 198:206), and numerical analysis (as in 198:323). Such background should include at least two semesters of calculus and one semester in each of the other areas.
  2. Working knowledge of high level languages (as in 198:111), data structures (as in 198:112), computer architecture and assembly language (as in 198:211), algorithm design and analysis (as in 198:344), and some elective courses in advanced undergraduate areas, such as programming languages and compilers (as in 198:314, 415), operating systems (as in 198:416), distributed systems (as in 198:417), information systems (as in 198:336), networks (as in 198:352), etc.

(Short descriptions of undergraduate courses offered by the department can be found in section 6.4 of this brochure.) 

Applicants who have adequate knowledge of the above subjects but who have not completed all academic courses in these areas may show proficiency by obtaining a high score on the GRE Subject Test in Computer Science. All applicants are required to take the aptitude part of the GRE examination (verbal, analytic and mathematical reasoning sections). The Computer Science Subject Test is optional, but highly recommended, particularly for applicants with a non-CS background, or who have been out of school for several years.

Criteria for admission currently include:

  1. An academic record (undergraduate and previous graduate work) that shows distinction (B+ or higher) in Computer Science, Mathematics and related fields. (The mean GPA for a recent entering class of students was 3.62; this included Master's and PhD students, with and without financial aid. )
  2. A high score on all the GRE examinations required, and the TOEFL exam in the case of foreign students. (The mean GRE scores for a recent entering class of students were: Verbal 150, Quantitative 160, the mean TOEFL score was 92, IBT S >21 IBT L >21.)
  3. Strong letters of recommendation.
  4. A clear statement, about one page in length, outlining the reasons why the applicant wishes to pursue graduate study in computer science. (If appropriate, please specify one or more areas of particular interest, to help us assign advisors. See section 5 for a list of areas.)
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