Study Plans for M.S.

Here is a sample study plan:

study plan

Upon entering the M.S. program, each student will be assigned a faculty advisor whose role is:

  • Help the student design an overall study plan that satisfies the program requirements, based on their interests.
  • Be the person the student consults before registering each semester

Students may change advisors as they become more familiar with the program (in particular if the Master's thesis option is chosen).

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.

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."

We suggest 4 study tracks for students in the MSCS program

  • Track 1: Machine Learning
  • Track 2: Vision and Graphics
  • Track 3: Systems
  • Track 4: Security

A chart showing the courses available in each area can be seen in the following image:

It also contains a sample Machine learning track that can be obtained by selecting a subset of the courses enclosed in ovals.

MLtrack

Program Course Requirements:

MS Students has to complete 30 credits (10 courses) divided as follows:

  • Breadth Requirement: As a "breadth requirement", each student must take at least two courses from each of categories A and B, and complete them with a grade of B or better.
  • Four additional courses from the union of categories A and B (For the thesis option the 701/702 credits can count as two of these four courses)
  • Two other courses, each of at least 3 credits. This can include:
    • graduate CS courses and seminars 
    • undergraduate courses that are accepted for graduate credit
    • approved courses in other departments
  • Besides the courses, a student has to write an Essay or a Thesis (see details in Section 5)

To complete the MS degree, students must present 30 credits satisfying the requirements listed above, and yielding at least a B average.

Starting Fall 2017, MS students will be allowed to use up to three credits of independent studies to meet graduation requirements.

Courses that are relevant to the graduate program in computer science may also be taken in the following Rutgers programs: Cognitive Science, Computer and Electrical Engineering, Industrial Engineering. Linguistics, Mathematics, Philosophy, Psychology, and Statistics.

Courses that are relevant to the graduate program in computer science may also be taken in the following Rutgers programs: Cognitive Science, Computer and Electrical Engineering, Industrial Engineering. Linguistics, Mathematics, Philosophy, Psychology, and Statistics. A list of relevant courses in some of these departments is given below in Section 6.3. Students may also take courses of special interest to them at Princeton University (Computer Science, Electrical Engineering, Philosophy), in accordance with a cooperative arrangement between Rutgers and Princeton (see section 8.2).