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MS Computer Science

Program Objectives

MS Computer Science (CS) is a research-based degree program for candidates with at least sixteen years education in the field of computing. The MS-CS is aimed at those students who want to extend their knowledge to a more advanced and highly specialized material that reflects current research trends in cutting edge of various CS disciplines. The program prepares the students for not only the industry but also would give them the required knowledge to prepare them for doctoral level degrees. Although the MS-CS is an independent program, however, research work developed in MS program can be stretched and made more comprehensive to serve as the research base for a PhD in CS provided the candidate fulfills all the requirements of the Institute and the HEC. The MS Computer Science will enable the students to:

  • Have a solid understanding of computational theory and foundational mathematics
  • Have substantial exposure to advanced topics in multimedia systems, software engineering, networks, computer architecture, and operating systems.
  • Prepare students to conduct research in computer science with advanced training in selected areas
  • Increase the opportunities for advanced positions in computing profession 

Eligibility Criteria

The candidates with at least sixteen years of education in the relevant disciplines are eligible for admission. Following are the basic requirements for admission to the MS-Computer Science program:

  1. 4 year BCS / BIT / BE or BS (Telecom, Electrical, Electronics) with a minimum of 130 credit hours from an HEC recognized university having scored at least 3.0/4.0 CGPA
  2. 2 year Master's degree in Computing/ IT (awarded after 16 years of education) with an aggregate of 60% marks from an HEC recognized university
  3. A minimum of 50% marks in GAT-General conducted by the National Testing Service (NTS)

Scheme of Courses

The MS Computer Science program comprises of a minimum of 30 credit hours which are to be completed in a minimum of three semesters. The distribution of the core and elective courses is given below: Core : 9
Elective : 15
Research thesis: 06
Total credit hours: 30

Semester Wise Breakup of Courses

Semester 1

S.No Code Course Credits
1 CS-711 Advance Theory of Computation 3
2 CS-712 Advance Algorithm Analysis 3
3 CS-741 Research methods for Computer Science 3
4 CS Elective 1 3

Semester 2

S.No Code Course Credits
1 CS Elective 2 3
2 CS Elective 3 3
3 CS Elective 4 3
4 CS Elective 5 3

Semester 3

S.No Code Course Credits
1 CS-749 Research Thesis/Dissertation 6

List of Electives for MS/PhD Computer Science

List of elective courses is given below. The Institute may add elective courses depending upon the demand and availability of resources. Further, it is important to note that a specialization will only be offered if at least 40% of the students from that class / batch register for it.

Multimedia Systems and Graphics

Code Course Credits
CS-751
CS-752
CS-753
CS-754
CS-756
CS-757
CS-758
CS-759
CS-760
CS-761
CS-762
CS-763
Advanced Digital Signal Processing
Advanced Digital Image Processing
Advanced Multimedia Systems
Advanced Computer Vision
Advanced Computer Graphics
Multimedia and Hypermedia System
Virtual and Augmented Reality
Advance Human Computer Interaction
Geographical Information Systems
Computer Animation
Multimedia Database
Biometric Systems
3
3
3
3
3
3
3
3
3
3
3
3

Computer Networks

Code Course Credits
CS-771
CS-772
CS-773
CS-774
CS-775
CS-776
CS-778
CS-779
CS-780
Advanced Computer Networks
Cryptography and Network security
Advance topics in Network Security
Distributed computing
Probabilistic graphic models
Network Management
Cloud Computing
Grid Computing
Advanced Operating Systems
3
3
3
3
3
3
3
3
3

Software Engineering

Code Course Credits
CS-721
CS-722
CS-723
CS-724
CS-725
CS-726
CS-727
 
Advanced Software Project Management
Requirement Engineering
Software System Architecture
Software System Quality
Formal Methods in Software Engineering
Advance topics in software engineering
Data Mining
3
3
3
3
3
3
3

Artificial Intelligence

Code Course Credits
CS-731
CS-732
CS-733
CS-734
CS-735
CS-736
CS-737
CS-738
Natural Language Processing
Machine Learning
Computer Vision
Neural Networks
Probabilistic graphic models
Expert systems
Fuzzy systems
Advance topics in AI
3
3
3
3
3
3
3
3

Note: The institute may add or remove the electives according to the resources available.