Post-Graduate Programmes in CSE


The International Institute of Information Technology has a number of Post-Graduate programmes at the Masters and the Ph.D. levels. The M.Tech. programmes involve an optional project work. The MS by Research and Ph.D. programmes are completely research oriented. In Computer Science and Engineering, the institute has M.Tech, MS by Research, and Ph.D. programmes. The curriculum and other details of the programme are given in thsi document.

Curriculum

Master of Technology (M. Tech.) in CSE

Semester I
CS3000 Advanced Problem Solving 4-0-4-6
CS3301 Computer Systems 4-1-3-6
CS3001 Scripting & Computer Environments 2-0-2-3
MA3201 Discrete Mathematics & Algorithms       3-0-2-4

Semester II
CS3105 Algorithms 3-0-2-4 
CS3400 Database Management Systems 3-0-2-4
CS3600 Software Engineering 3-0-2-4

Elective
CS3002 Software Technologies (H1) 2-0-2-2

Semester III
CS3350 Computer Networks 3-0-2-4 

Elective

Elective

Elective

Semester IV
          Elective

Elective

Elective

Elective

Note:

  1. A student can get a waiver from any of the courses listed as compulsory if he or she can demonstrate the knowledge of the course. The instructor who is offering the course should be approached with this request. The instructor will evaluate the student and recommend to the Dean. The student should then substitute that course slot with another course being offered that semester of level 3000 or above.
  2. A student can substitute a maximum of 5 electives in Semesters III and IV with projects done under a faculty member. A student can also combine these into a single 20 credit M.Tech. project. This project can be done at an industry with a co-advisor in the institute to ensure the academic content of the project.

M. S. by Research in CSE

Semester I

Breadth Elective
                  Breadth Elective

Stream Elective

Stream/Breadth Elective

Semester II


Breadth Elective

Stream Elective

Elective

Stream/Breadth Elective

Literature Survey

Semester III

CS9900 MS Thesis 0-0-16-16

Semester IV

CS9900 MS Thesis 0-0-16-16

Ph. D.

The Ph. D. programme does not have any suggested curriculum outline. The students should take courses to build up their background for the breadth qualifiers and to equip them for the research work. They must satisfy the course requirements before being eligible to take the depth qualifiers.

Streams and Courses

The stream names and the courses that belonging to the stream are listed below. In all cases, there is a basic course in the stream and there are several advanced courses. The basic courses of each stream is listed in boldface followed by the advanced courses. Courses shown in italics are auxiliary stream courses ``owned'' by another stream.
  1. AI/NLP: Artificial Intelligence. Introduction to NLP, Machine Translation & Information Extraction, Semantics, Speech Processing, Multi-Agent systems. PR, SC.
  2. Data Engineering: Introduction to DBMS. Distributed DBMS, Database Implementation, Data Warehousing & Data Mining, Web Data & Knowledge Management. PR, MAS.
  3. Visual Information Processing: Image Processing or Computer Graphics. Computer Graphics, Image Processing, Computer Vision, Multimedia, Soft Computing, Pattern Recognition. AI, DSP.
  4. VLSI & Embedded Systems: Microprocessor Design. Embedded Systems, VLSI 1, VLSI 2, VLSI 3, VHDL. DSP.
  5. Communications: Digital Signal Processing or Telecommunications, Digital Communications, Advanced Communications, Cryptography. Networks, IP.
  6. Computer Systems: Computer Networks. Performance Evaluation, Advanced Computer Networks. Microprocessors, ES, DC.
  7. Theory and Algorithms: Algorithms Theory of Computation, Formal Methods, Advanced Algorithms. FM.
  8. Programming Languages: Principles of Programming Languages. Programming Language Processors, Formal Methods.
  9. Optimization & Numerical Analysis: PR, SC.

List and Schedule of Courses

The semester-wise list of elective/flexi-core courses of 3000 or higher level offered at the institute are given below. This list is indicative only. The offerings may change in the future.
Monsoon Semester:

Introduction to NLP

Distributed DBMS
Web Data & Knowledge Management
Database Implementation

Digital Image Processing
Multimedia Systems
Pattern Recognition

Principles of Programming Languages
Programming Language Processors

Microprocessor Design
VLSI Algorithms
Digital Design with VHDL
Analog and Mixed Signal Design

Digital Communications

Computer Networks

Software Engineering
Spring Semester:

Artificial Intelligence
NLP Semantics
Machine Translation & Information Extraction
Multi-Agent Systems
Speech Processing

Database Management Systems
Data Warehousing & Data Mining
Performance Evaluation of Computer Systems
Applied Data Mining

Computer Graphics
Computer Vision
Advanced Signal & Image Processing
Soft Computing

Computer Architecture
Embedded Systems
Mathematical Methods in VLSI Design

Digital Signal Processing
Telecommunications
Cryptography

Algorithms
Theory of Computation
Advanced Algorithms
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