M. Phil Computer Science educates students about advanced knowledge of various elements of Computers which include technical and detailed study of computing and its applications. The course is aimed to explore various research avenues and provide professional attributes to the students.
The delivery method of the course comprises of classroom lectures, case study reports, workshops, seminars, guest lectures from successful personnel, research work and industrial visits to help students get familiar with work strategies being followed in the industry.
The scheme of assessment remains 25% weightage of internals and 75% weightage of externals which include project work, viva-voce, theoretical and practical examination.
Syllabus
The subjects in the syllabus of M. Phil. Computer Science cover topics which train students to tackle real life problems occurring in computing industry. An overview of the syllabus is given below:
|
Subject |
Topics Covered |
Objective |
|---|---|---|
|
Research Methodology |
Thesis Writing, Analysis of Algorithms, Formal Languages and Automata, Probability and Statistical Analysis, Logics, Relations and Functions |
To impart the basic concepts on algorithms, formal languages and Automata, probability and statistics, logic, relations and functions which are required for research and to give knowledge on thesis writing. |
|
Concepts In Computer Science |
Computer Architecture, Distributed Databases, Communication Protocols, Computer Graphics and Multimedia, Web Technology |
To impart knowledge on the some of the advance topics in Computer Science such as computer architecture, distributed databases, communication protocols, Computer graphics and Web Technology. |
|
Artificial Intelligence & Expert Systems |
Artificial Intelligence, Knowledge Representation, Natural language processing, Expert Systems, Knowledge Base and chaining functions |
The objective of this course is to educate students about different types of expert systems in computers. |
|
Simulation And Modeling |
Introduction to Simulation, Statistical Models in Simulation, Random - Number Generation, Input Modeling : Data Collection, Comparison of Two System Designs |
Students are taught about the simulation and modelling techniques. |
|
Grid Computing |
Grid Computing: Early Grid Activities, Merging the Grid Services Architecture with the Web Services Architecture, Open Grid Services Infrastructure (OGSI), The Grid Computing Tool Kits |
Students learn grid computing and its background and detailed knowledge. |
|
Data Mining |
Introduction to Data Mining, Data Processing: Cleaning, Concept description, Classification and prediction, Multidimensional analysis and descriptive mining of complex data objects |
The objective of this course is to provide students a detailed knowledge about concepts of data mining. |
|
Wireless Networks And Security |
Overview of Wireless Networks, Network Planning, CDMA Technology, IEEE 802.11 WLAN’s, Case studies |
The subject covers various topics of wireless networks and its mechanism. |
|
Object Oriented Database Systems |
Object Oriented System, Object Modeling, Object -Oriented- Databases, Transactions, Parallel Databases |
The main objective of the course is to make students familiar with object oriented database systems. |
|
Artificial Neural Networks |
Characteristics of biological Neuron, Back Propagation network, Statistical methods |
The subject covers the concepts of artificial neural networks along with its principles and elements. |
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