M.tech 1st Year Syllabus

Master of Engineering
(Two Year Full Time Degree Program)
SYLLABUS
(M. Tech. First Year)
School of Computer Science and Engineering

Shri Mata Vaishno Devi University Katra
(April 2018)
SYLLABUS of M. Tech. CSE 1st Year Batch 2017-18
ABBREVIATIONS / CODES / NOMENCLATURE
Course Code Convention

SCT – LSAY Course Code for various Courses / Subjects
SC: School Code
Example T: Course Type Code (Lecture/Studio/Practical/Project etc.)
ALL 9101 L: Course Level (1, 2, 3, 4 & 5 for First, Second years …)
ALP 9102 SA: Study Area / Sub Area
ALS 9110 Y: Semester Wise Course Number
CSE School Code (SoCSE)
L Lecture
P Practical
E Elective
C Colloquium
D Project Based
T Training
S Self Study
N Non Credit
V Special Lecture Topic
Teaching Scheme Convention
L Lecture
T Tutorial
P Practical
C Course Credit
Evaluation Scheme Convention
Minor (Mid Term Exams / Tests) I & II
Major Semester End Examination (ESE)
FFCS Fully Flexible Credit System
CBCS Choice Based Credit System
Page 2 of 13
Teaching & Examination Scheme
M.Tech. , Semester-I , FIRST Year

Teaching & Credit Scheme Evaluation & Examination Scheme
S. Subject Title of the L T P S Total C Minor E Major E Internal Minor Major Total
No. Code Subject Periods/week Duration Duration Marks (I+II) ESE Marks
(Hours) (Hours) Marks Marks
1. CSL6025 Advanced 3 0 2 8 4 1 3 10 40 50 100
Programming
2. CSL6103 Artificial 3 1 0 8 4 1 3 10 40 50 100

Intelligence & fuzzy
Logic
3. CSL6142 Modeling & 3 1 0 8 4 1 3 10 40 50 100
Simulation
4. CSL6073 Network 3 1 0 8 4 1 3 10 40 50 100
management
5. CSL6046 Discrete 3 1 0 8 4 1 3 10 40 50 100
Mathematical
Structure
SUB TOTAL 15 4 2 40 20 50 200 250 500
M.Tech. , Semester-II, FIRST Year

Teaching & Credit Scheme Evaluation & Examination Scheme
S. Subject Title of the L T P s Total C Minor E Major E Internal Minor Major Total
No. Code Subject Periods/week Duration Duration Marks (I+II) ESE Marks
(Hours) (Hours) Marks Marks
1. CSL6062 Advanced 3 1 0 8 4 1 3 10 40 50 100
Computer
Architecture
2. CSL6104 Neural Networks 3 1 0 8 4 1 3 10 40 50 100
& Probabilistic
Reasoning
3. CSL6083 Advanced 3 0 2 8 4 1 3 10 40 50 100
DBMS
4. CSL6084 Data Mining 3 1 0 8 4 1 3 10 40 50 100
& Data
Warehousing
5. CSE6113 Elective-1 3 0 2 8 4 1 3 10 40 50 100
Image
Processing
SUB TOTAL 15 4 2 20 50 200 250 500
Page 3 of 13
CSL6025 Advanced Programming Pre Requisites

Version R-01 Co-requisites
L T S/P C Minor Major Internal Minor-I Minor-II Major Total
Duration Duration Marks Marks Marks Marks Marks
3 0 2 4 1 Hours 3 Hours 10 20 20 50 100
COURSE OUTCOMES
After successful completion of this course, students shall be able to;
1. Understand the syntax, control structures, data structures of java programming
language. Ability to demonstrate simple Java programmes.
2. Ability to code any given algorithm, or provide a solution to complex-real-life-
problem using JAVA language
Ability to build Desktop Applications with GUI(Graphical User Interface) and
Database connectivity to create real-life/business solutions
3. Inculcating the ability to enjoy coding and build simple games like Tic-Tac-Toe etc.
4. Ability to use Industry standard IDEs (Integrated Development Environments) like
NetBeans/Eclipse for coding, debugging etc.
5. Ability to code and manage at least a few thousand lines of code which enforces the
use of Industry best practices like documentation etc
UNIT 1 Introduction to Java Programming

Introduction to Java Programming: Basic constructs of OOPS, Data types, Operators &
Control Statements, Classes and Methods, Inheritance and Packages, Exception handling:
Fundamental of Exception, Types of Exceptions, creating of Exceptions, Multithreaded
programming and I/O.
UNIT 2 AWT and Event handling
AWT and Event handling: Fundamental of AWT working with Frames Fonts Layout
Managers, Buttons, Menus, Event Classes, Interfaces. JDBC: Introduction to SQL, ODBC,
JDBC API Application Architecture steps for creating DSN, Interfaces.
UNIT 3 Swing and Java
Swing and Java: Introduction to JFC, Features, Handling Components with Swing, listener
Interfaces. Introduction of beans creation of own bean, Enterprise Java beans (EJB) other
bean development tools. Remote Method Invocation: Introduction Comparison of Distributed
and Non-distributed Java programs, RMI Packages, RMI Enhancements.
UNIT 4 Networking
Networking: Connecting to a Server, Implementing Servers, Sending E-Mail making URL
Connections, Advance Socket programming: Socket Timeouts, Interruptible Sockets,
half/Close Internet Addresses. Security: Class Loaders, Security Manager and Permissions,
User Authentication Digital Signatures, Code Signing JAR file Signing, Encryption,
Internationalization: Locales Number formats, Message Formatting, Text files and Character
Sets, Resources Bundles Classes, Native Methods.
SUGGESTED BOOKS:
1. Java-2 Volume II by Cay S.Horstmann, Cornell, Pearson Education
2. The Complete Reference Java 2 (5th Ed.), Herbert Schildt: TMH
3. Java how to Program (6th Ed.) Deitel and Deitel: PHI Publication
Page 4 of 13
CSL 6103 Artificial Intelligence and Fuzzy Pre Requisites

Logic

3 1 0 4 1 Hours 3.0 Hours 10 20 20 50 100
COURSE OUTCOMES
1. Familiarize with propositional and predicate logic and their roles in logic programming;
2. Understand logical programming and write programs in declarative programming style;
3. Learn the knowledge representation and reasoning techniques in rulebased systems, case-
based systems, and model-based systems;
COURSE CONTENT
UNIT 1 Introduction:
AI History and applications. Overview of AI application areas: game playing, automated
reasoning and theorem proving, expert systems, natural language understanding, planning
and robotics, machine learning and Alan Turing Test.
UNIT 2 The Propositional and Predicate Logic:
Symbol and sentences, the semantics of the Propositional Calculus & Predicate Calculus.
Inference Rules and Theorem Proving. Axioms, Literals, Horn clause & Clausal forms.
UNIT 3 Reasoning:
Inductive, Deductive, Abductive and Default reasoning. More examples on Resolution proof.
UNIT 4 Problem Solving as Search:
Structures and strategies for state space search. Algorithms for Heuristic search, Heuristic
evaluation functions, Heuristic search and expert systems, using Heuristics in games, Time &
Complexity issues etc.
UNIT 5 Knowledge Representation:
Knowledge representation Techniques; a survey of network representation; conceptual
graphs; structured representations; frames, scripts; issues in knowledge representation:
hierarchies, inheritance, exceptions; efficiencies.
UNIT 6 Knowledge Elicitation and Knowledge Acquisition:
An overview of the induction methods, types and tools. Stages in Knowledge acquisition
with examples. Analyzing, coding, documenting and diagramming. Scope of knowledge.
UNIT 7: Expert Systems:
Overview of expert system technology; rule-based expert systems; Construction of ES.
Components of an ES. The explanation facility. Rule-based formation and forward and
backward chaining techniques for problem solving.
UNIT 8 Reasoning with uncertain and incomplete information:
The statistical approach to uncertainty, Bayesian reasoning, the Dempster-Shafer theory of
evidence, Certainty Factor, Reasoning with Fuzzy sets.
SUGGESTED BOOKS:
1. Artificial Intelligence: Strategies and techniques for complex problems solving by George
Luger, Addison-Wesley, 2003.
2. Artificial Intelligence - A Modern Approach by Stuart Russell & Peter Norvig, Prentice
Hall.
3. Artificial Intelligence - A New Synthesis by Nils J. Nilsson, Morgan Kaufmann Publishers.
Page 5 of 13
CSL 6142 Modeling & Simulation Pre Requisites

3 1 0 4 1 Hours 3.0 Hours 10 20 20 50 100
COURSE OUTCOMES
1. Understand different methods for random number generation
2. Have a clear understanding of the need for the development process to initiate the real
problem.
3. Have a clear understanding of principle and techniques of simulation methods informed
by research direction.
UNIT 1 Concepts of systems

Concepts of systems, Models, and Simulation, Distributed Lag Model, Cobweb Models,
The process of a simulation-Study, Exponential Growth Models, Exponential Decay
Models, Type of simulation, Discrete-Event Simulation: Time-Advance Mechanisms,
Components and Organization of a Discrete-Event Simulation Model. Monte Carlo
Method. Simulation of Single-Server Queuing System, Simulation of an Inventory System.
UNIT 2 Continuous Simulation
Continuous Simulation: Pure-pursuit Problem. Random Number Generators: Linear
Congruential Generators, Other kinds of Generators, Testing Random-Number Generators.
Generating Random Variates: General Approaches, Continuous and Discrete distributions.
UNIT 3 Introduction to GPSS
Introduction to GPSS, General Description, GPSS block-diagram, Simulation of a
Manufacturing Shop, SNA, Function, Simulation of a Supermarket, GPSS Model of a
Simple Telephone system.
UNIT 4 Output Data Analysis for a Single System
Output Data Analysis for a Single System: Transient and Steady-State Behavior of a
Stochastic Process, Type of Simulations with regard to output Analysis and Statistical
Analysis for Testing Simulation, Verification and Validation of Simulation. An
introduction of different types of simulation languages.
SUGGESTED BOOKS:
1. G.Gorden, “System Simulation”, Pearson Education
2. Law and Kelton, “Simulation Modeling and Analysis”, McGraw Hill
3. N.Deo,”System Simulation with Digital Computer”, Prentice Hall of India
4. Fred Maryanski, “Digital Computer Simulation”, CBSPD
Page 6 of 13
CSL 6046 Pre Requisites

Discrete Mathematical Structures

3 1 0 4 1 Hours 3.0 Hours 10 20 20 50 100
COURSE OUTCOMES
1. Write an argument using logical notation and determine if the argument is or is not
valid.
2. Demonstrate the ability to write and evaluate a proof or outline the basic structure of
and give examples of each proof technique described.
3. Understand the basic principles of sets and operations in sets.
4. Prove basic set equalities.
5. Apply counting principles to determine probabilities
Unit 1 : Basic counting principle

Basic counting principle: Pigeonhole principle, inclusion - exclusion principle, recurrence
relations, generating functions. Construction of finite fields, codes grammars and language,
elements of logic.
Unit 2 : Undirected and direct graphs

Undirected and direct graphs, modelling with graphs, trials and cycles, connectivity and trees.
Unit 3 : Graph algorithms

Graph algorithms: BFS, DFS, shortest path, optimal spanning trees, matching, job assignment
problem, optimal transportation through flows in networks.
Unit 4 : Introduction to Operations research

Introduction to Operations research. Linear Programming: Principles of simplex Method.
Simplex method in tabular from. Duality and Dual simplex Method. Degeneracy and cycling.
SUGGESTED BOOKS:
1. K. H. Rosen, Discrete Mathematics and its applications, McGraw-Hill, 2007.
2. Kolman, Busby and Ross, “Discrete Mathematical Structures”, Pearson Education.
Page 7 of 13
CSL 6073 Network Management Pre Requisites

3 1 0 4 1 Hours 3.0 Hours 10 20 20 50 100
COURSE OUTCOMES
1. Describe network management and the network management architecture
2. Explain the various functions of network management.
3. Gain in-depth theoretical and practical knowledge of network management, and in
particular of SNMP (Simple Network Management Protocol).
4. Compare a number of variations of the network management architecture
Unit 1 Data Communications and Network Management Overview

Data Communications and Network Management Overview: Review of Computer Network
Technology, Basic Foundations: Standards, Models, and Language Networking Components,
Overview of Network Management, Network Management Strategies, Configuration of
client, Server and Infrastructure Components
Unit 2 Network Management

SNMPv1, SNMPv2, SNMPv3 Network Management: Organization, Information Models,
Communication and Functional Models, Secure SNMPv3, RMON, RMON2
Unit 3 Network Management Tools and Systems
Network Management Tools and Systems : Network Management Applications, Broadband
Network Management, ATM Network, Telecommunications Management Network, Web-
Based Management , Network Management Initiatives.
SUGGESTED BOOKS:
1. Network Management: Principles and Practice, 1/e, Mani Subramaniam, Pearson
Educations
Page 8 of 13
CSL6062 Advanced Computer Architecture Pre Requisites

3 1 0 4 1 Hours 3 Hours 10 20 20 50 100
COURSE OUTCOMES
1. Know the classes of computers, and new trends and developments in computer
architecture
2. Understand pipelining, instruction set architectures, memory addressing.
3. Understand the performance metrics of microprocessors, memory, networks, and
disks
4. Understand exploiting ILP using dynamic scheduling, multiple issue, and speculation.
Unit-I: Parallel computer models
The state of computing, System Attributes to Performance: Clock Rate and Cycles per
Instruction, Performance Factors, System Attributes, MIPS Rate, Performance factors versus
System attributes, Throughput Rate, Programming Environment, Implicit & Explicit
Parallelism, MIPS Ratings and Performance Measurement, Classification of parallel
computers, Multiprocessors and Multi-Computers: Shared- Memory Multiprocessors: UMA
Model, Symmetric & Asymmetric Multi Processors Distributed Memory Multi-Computers,
NUMA and COMA models for Multiprocessors
Unit-II: Program and network properties
Conditions of parallelism, Data and resource Dependences, Control Dependence, Resource
Dependence, Bernstein’s Conditions for Parallel Processing, Hardware and software
parallelism, Program partitioning and scheduling, Grain Size and latency, Program flow
mechanisms, Control flow versus data flow, Data flow Architecture, Demand driven
mechanisms, Comparisons of flow mechanisms
Unit-III: System Interconnect Architectures
Network properties and routing, Node Degree and Network Diameter, Bisection Width, Data
Routing Functions, Permutations, Perfect Shuffle and Exchange, Hypercube Routing
Functions, Broadcast and Multicast, Network Performance
Unit-IV: Static and Dynamic Interconnection Networks
Static interconnection Networks: Linear Array, Ring and Chordal Ring, Barrel Shifter, Tree
and Star, Fat Tree, Mesh and Torus, Systolic Array, Hypercubes, Cube-Connected Cycles, k-
array n-cube networks, Network Throughput, Comparison of characteristic of various static
interconnection networks, Dynamic interconnection Networks: Digital Buses, Switch
Modules, Multistage Networks, Omega Network, Baseline Network, Crossbar Network.
Unit-V: Advanced processors
Advanced processor technology, Design Space of Processors, Instruction pipelines,
Processors and Coprocessors, Instruction-set Architectures, Complex Instruction Sets,
Reduced Instruction Sets, Architectural Distinctions, CISC Scalar Processors, Representative
CISC Scalar Processors, RISC Scalar Processors, Representative RISC Scalar Processors,
Superscalar Processors, VLIW Architectures, Vector and Symbolic processors
SUGGESTED BOOKS:
1. Kai Hwang, “Advanced computer architecture”; TMH. 2000
2. Hwan and Briggs, “Computer Architecture and Parallel Processing”; MGH. 1999
3. D. A. Patterson and J. L. Hennessey, “Computer organization and design”, Morgan
Kaufmann, 2nd
Page 9 of 13
CSL6104 Neural Networks and Probabilistic Pre Requisites

Reasoning

3 1 0 4 1 Hours 3 Hours 10 20 20 50 100
COURSE OUTCOMES
1. The student will be able to obtain the fundamentals and types of neural networks.
2. The student will have a broad knowledge in developing the different algorithms for
neural networks. Student will be able analyze neural controllers
3. Student will have a broad knowledge in Fuzzy logic principles.
4. Student will be able to determine different methods of Deffuzification
UNIT 1 Reasoning with uncertain information:

Review of Probability theory, conditional Probabilities, Probabilistic Inference, Conditional
Independence, Bays Networks, Bayesian Reasoning, Dempstor-Shafer theory of evidence,
Probablistic Inference in Polytrees, Evidence above and evidence below, non-monotonic
systems, Certainty Factor.
UNIT 2 Genetic Algorithms: Introduction:
Operatiors: reproduction, crossover & mutation. Fitness function, Simple program
demonstration. G.A. software packages.
UNIT 3 Neural Networks:
Biological neural system, Artificial Intelligent Systems, Modeling human performance.
Foundation for connectionist networks. Activation functions, McCulloch-Pitts Neurons,
Perceptron Learning, The Window-Hoff Procedure, Generalised Delta Procedure, Error-
correction Procedure. Backpropagation learning, Competitive learning, Hebbian learning.
Supervised and Unsupervised learning, Kohonon Self-Organising Maps, Reinforcement
learning, Neural Networks: promises and problems. Neural Networks application.
SUGGESTED BOOKS:
1. Neural Networks : A Comprehensive Foundation by Simon Haykin, Prentice Hall of India
2. Artificial Intelligence – A New Synthesis by Nils J.Nilsson, Morgon Kaufmann Publishers.
Page 10 of 13
CSL6083 Advanced DBMS Pre Requisites

3 0 2 4 1 Hours 3 Hours 10 20 20 50 100
COURSE OUTCOMES
1. Explain in detail DBMS architecture.
2. Explain in detail query processing and techniques involved in query optimization.
3. Explain the principles of concurrency control.
4. Explain the principles of recovery management.
5. Know recent developments and active research topics in database.
UNIT 1 Storage and File structures
Storage and File structures: RAID, tertiary storage, storage access, file organization, Data
dictionary storage
UNIT 2 Query Processing & Optimization
Query Processing: Overview, query cost, selection operation, sorting, join operation,
Other operations, evaluation of expressions
Query Optimization: Overview, Transformation of relational expression, estimating statistics
of expression results, choice of evaluation plans, materialized views
UNIT 3 Database system architectures
Database system architectures: Centralized and client-server architectures, parallel systems,
distributed systems
Parallel databases: Introduction, I/O parallelism, interquery parallelism, intraquery
parallelism, intraoperational parallelism, interoperational parallelism, design of parallel
systems
Distributed databases: homogeneous and heterogeneous databases, distributed data storage,
distributed transactions, commit protocols, concurrency control in distributed databases,
distributed query processing, heterogeneous distributed databases, directory systems
UNIT 4 Advance transaction processing
Advance transaction processing: Transaction processing monitors, Real time transaction
systems, Long duration transactions
SUGGESTED BOOKS:
1. Silber Schatz. Korth, “Database System Concepts”, Tata Mc Graw Hill.
2. ShamKanth B. Navathe, “Fundamental of DataBase System”, Pearson Education.
3. C. J. Date, “An introduction to database systems”, Addison Wesley publishing
company
Page 11 of 13
CSL6084 Data Mining And Data Pre Requisites

Warehousing

3 1 0 4 1 Hours 3 Hours 10 20 20 50 100
COURSE OUTCOMES
1. Describe the fundamental concepts, benefits and problem areas associated with data
warehousing
2. Describe the various architectures and main components of a data warehouse.
3. Design a data warehouse, and be able to address issues that arise when implementing
a data warehouse.
UNIT 1 Overview of decision support systems
Overview of decision support systems: organizational need for strategic information, Failures
of past decision-support systems, operational versus decision-support systems, data
warehousing-the only viable solution, data warehouse defined. Data warehouse – The
building Blocks: Defining Features, data warehouse and data marts, overview of the
components, metadata in the data warehouse. Defining the business requirements:
Dimensional analysis, information packages - a new concept, requirements gathering
methods, requirements definition: scope and content.
UNIT 2 Principles of dimensional modeling
Principles of dimensional modeling: Objectives, From Requirements to data design, the
STAR schema, STAR Scheme keys, Advantages of the STAR Schema. Dimensional
Modeling: Updates to the Dimension tables, miscellaneous dimensions, the snowflake
scheme, aggregate fact tables, families of STARS.
UNIT 3 OLAP in the Data Warehouse
OLAP in the Data Warehouse: Demand for online analytical processing, need for
multidimensional analysis, fast assess and powerful calculations, limitations of other analysis
methods, OLAP is the answer, OLAP definitions and rules, OLAP characteristics, major
features and functions, general features, dimensional analysis, what are hypercubes? Drill-
down and roll-up, slice-and-dice or rotation, OLAP models, overview of variations, the
MOLAP model, the ROLAP model, ROLAP versus MOLAP, OLAP implementation
considerations.
UNIT 4 Data Mining Basics
Data Mining Basics: What is Data Mining, Data Mining Defined, The knowledge discovery
process, OLAP versus data mining, data mining and the data warehouse, Major Data Mining
Techniques, Cluster detection, decision trees, memory-based reasoning, link analysis, neural
networks, genetic algorithms, moving into data mining, Data Mining Applications, Benefits
of data mining, applications in retail industry, applications in telecommunication industry,
applications in banking and finance.
SUGGESTED BOOKS:
1. Paul Raj Poonia, “Fundamentals of Data Warehousing”, John Wiley & sons.
2. Sam Anahomy, “Data Warehousing in the real world: A practical guide for building
decision support systems”, John Wiley
3. Alex berson, Stephen J. Smith, “Data Warehousing, Data Mining & OLAp”, Tata
McGraw Hill
Page 12 of 13
CSL6113 Image Processing Pre Requisites

3 0 2 4 1 Hours 3 Hours 10 20 20 50 100
COURSE OUTCOMES
(1) Understand image formation for the acquisition of images.
(2) Get broad exposure of the various applications of image processing in industry, medicine,
agriculture etc.
(3) Get knowledge of existing algorithms for the processing of digital images.
(4) Apply knowledge/skills to solve industrial problems based on image processing.
COURSE CONTENTS
Unit-1 Introduction and Digital Image Fundamentals (7 Contact Periods)
Application of Image Processing, Image Processing definition, steps in image Processing,
Image Sensing and Acquisition, Image Sampling and Quantization, Spatial and Intensity
resolution-Effect of reducing spatial resolution, DPI, Effect of reducing image gray levels.
Basic relationships between pixels and adjacency
Unit-2 Intensity Transformation and Spatial Filtering (8 Contact Periods)
Basics of intensity transformation and spatial filtering, intensity transformation functions-
image negative, log transformation, power law; Piecewise-linear transformation functions-
contrast stretching, intensity level slicing, bit plane slicing; Histogram Processing-histogram
stretching, histogram equalization, Spatial Filtering, Spatial Correlation and Convolution,
Smoothing Spatial Filters, order statistic filters, Sharpening Spatial Filters- The Laplacian,
The Gradient-Robert cross gradient operator, Sobel operators
Unit-3 Image Restoration (4 Contact Periods)
Model of the image degradation/restoration process, Noise Models, Periodic Noise,
Estimation of noise parameters, Restoration in the presence of noise-spatial filtering- Mean
filters, Order-statistics filters, Median filter, Max and Min filters, Mid-point filter, Alpha-
trimmed mean filter, adaptive filters.
Unit-4 Color Image Processing (4 Contact Periods)
Introduction to the color image processing, color models: RGB, HSI, CMY/ CMYK;
Conversion of color models: converting colors from RGB to HSI, HSI to RGB, RGB to CMY
and CMY to RGB etc. Pseudo coloring of images.
Unit-5 Image Compression (7 Contact Periods)
Introduction to image compression, need of compression, methods of image compression:
coding redundancy, spatial and temporal redundancy, irrelevant information, models of
image compression, Huffman coding, Arithmetic coding, LZW coding, run-length coding,
block transform coding, JPEG compression, predictive coding
Unit-6 Image Segmentation (6 Contact Periods)
Fundamental, Point, Line and Edge detection, edge linking and boundary detection, Hough
transform, thresholding, region-based segmentation, region splitting and merging
SUGGESTED BOOKS
1. Rafael C. Gonzalez & Richard E. Woods, “Digital Image Processing”, 3rd edition,
Pearson Education.
2. David A. Forsyth, Jean Ponce, “Computer Vision: A Modern Approach”, Prentice
Hall
3. A.K. Jain, “Fundamental of Digital Image Processing”, PHI
Page 13 of 13

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Master of Engineering

(Two Year Full Time Degree Program)

School of Computer Science and Engineering

ABBREVIATIONS / CODES / NOMENCLATURE

Course Code Convention

Teaching & Examination Scheme

M.Tech. , Semester-I , FIRST Year

2. CSL6103 Artificial 3 1 0 8 4 1 3 10 40 50 100

M.Tech. , Semester-II, FIRST Year

CSL6025 Advanced Programming Pre Requisites

UNIT 1 Introduction to Java Programming

CSL 6103 Artificial Intelligence and Fuzzy Pre Requisites

Version R-01 Co-requisites

CSL 6142 Modeling & Simulation Pre Requisites

Version R-01 Co-requisites

UNIT 1 Concepts of systems

CSL 6046 Pre Requisites

Version R-01 Co-requisites

Unit 1 : Basic counting principle

Unit 2 : Undirected and direct graphs

Unit 3 : Graph algorithms

Unit 4 : Introduction to Operations research

CSL 6073 Network Management Pre Requisites

Version R-01 Co-requisites

Unit 1 Data Communications and Network Management Overview

Unit 2 Network Management

CSL6062 Advanced Computer Architecture Pre Requisites

Version R-01 Co-requisites

CSL6104 Neural Networks and Probabilistic Pre Requisites

Version R-01 Co-requisites

UNIT 1 Reasoning with uncertain information:

CSL6083 Advanced DBMS Pre Requisites

Version R-01 Co-requisites

CSL6084 Data Mining And Data Pre Requisites

Version R-01 Co-requisites

CSL6113 Image Processing Pre Requisites

Version R-01 Co-requisites

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