m.tech. (computer science and engineering)1. nils j. nilsson: “principles of artificial...
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Karnatak Law Society’s
GOGTE INSTITUTE OF TECHNOLOGY
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
M.Tech. (Computer Science and Engineering)
Second Semester
S.No. Course
Code Course
Credits Total
credits
Contact
Hrs/wk
Marks
L - T - P CIE SEE TOTAL
1. SCS21 Data Science and Analytics PC1 3 - 1 - 0 4 5 50 50 100
2. SCS22 Advances in Computer Networks PC2 3 - 1 - 0 4 5 50 50 100
3. SCS23 Artificial Intelligence & Agent
Technology PC3 3 - 1 - 0 4 5
50 50 100
4. SCS24 Machine Learning Techniques PC4 4 - 0 - 0 4 4 50 50 100
5. SCS25X Elective – II PE- B 4 - 0 - 0 4 4 50 50 100
6. SCS26 Machine Learning Laboratory 0 - 0 - 2 2 4 25 25 50
7. SCS27 Seminar-II 0 - 1 - 0 1 - 25 - 25
8. PTA28 Mini Project-II Mandatory 0 - 0 - 2 2 4 25 - 25
Total 25 31 325 275 600
ELECTIVE – II
SCS251 Wireless Networks & Mobile Computing
SCS252 Network Programming and Internet
Technologies
SCS253 Information Storage Management
SCS254 Web Security
Second Semester
Data Science And Analytics
Subject Code: SCS21 Credits: 4
Course Type: PC1 CIE Marks: 50
Hours/week: L – T – P 3 – 1 – 0 SEE Marks: 50
Total Hours: 40 SEE Duration: 3
Course Learning Objectives:
1. To introduce the fundamentals of Data Analytics life cycle.
2. To present Analytics methods and tools of analysis.
Detailed Syllabus:
UNIT I 08 hours
Introduction to Big Data Analytics: Big Data Overview, State of the Practice in Analytics,
Key Roles for the New Big Data Ecosystem, Examples of Big Data Analytics.
Data Analytics Lifecycle: Data Analytics Lifecycle Overview, Phase 1: Discovery, Phase 2:
Data Preparation, Phase 3: Model Planning, Phase 4: Model Building, Phase 5: Communicate
Results, Phase 6: Operationalize, Case Study: Global Innovation Network and Analysis
(GINA)
UNIT II 08 hours
Advanced Analytical Theory and Methods: Association Rules: Overview, Apriori
Algorithm, Evaluation of Candidate Rules, Applications of Association Rules, An Example:
Transactions in a Grocery Store, Validation and Testing, Diagnostics.
Self-Study: Case study: Global innovation network and analysis (GINA) 02 Hours
UNIT III 08 hours
Advanced Analytical Theory and Methods: Classification: Diagnostics of Classifiers,
Additional Classification Methods, Time Series Analysis, Overview of Time Series Analysis,
ARIMA Model, Additional Methods.
Self-Study: Review of Basic Analytic methods using R: Introduction to R, Exploratory
data analysis, statistical methods for evaluation. 02 Hours
UNIT IV 10 hours
Advanced Analytical Theory and Methods: Text Analysis: Text Analysis Steps, A Text
Analysis Example, Collecting Raw Text, Representing Text, Term Frequency - Inverse
Document Frequency (TFIDF), Categorizing Documents by Topics, Determining Sentiments,
Gaining Insights.
UNIT V 10 hours
Advanced Analytics - Technology and Tools: In-Database Analytics: S Analytics for
Unstructured Data, The Hadoop Ecosystem, NoSQL,QL Essentials, In-Database Text
Analysis
Prerequisite:
1. Big data management
2. Artificial intelligence
3. Probability and Statistics
TEXT BOOK:
1. Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and
Presenting Data by EMC Education.
REFERENCE BOOKS:
1. Data Science from Scratch: Joel Grus, O’reilly Publication.
2. Data Science for Business: Foster Provost.
3. Doing Data Science - Cathy O'neil.
Course Outcome (Cos): Students should be able to:
1. To explain the importance of Data Analytics.[L1]
2. To discover, prepare and analyze data in a given application domain.[L3]
3. To demonstrate application of text analysis and discover patterns.[L3]
Scheme of Continuous Internal Evaluation (CIE):
Components Average of best
two tests out of
three
Average of
two
assignments
Quiz/Seminar/
Project
Total
Marks
Maximum
Marks
30 10 10 50
Scheme of Semester End Examination (SEE):
Semester end examination will be conducted for 100 marks which will be converted into 50
marks. SEE question paper will have two compulsory questions (any 2 units) and choice will
be given in the remaining three units.
Second Semester
Advances in Computer Networks
Subject Code: SCS22 Credits: 4
Course Type: PC CIE Marks: 50
Hours/week: L – T – P 3 – 1 – 0 SEE Marks: 50
Total Hours: 40 SEE Duration: 3 Hours
Course Objectives:
1. To become familiar with the basics of Computer Networks
2. To understand various Network architectures
3. To learn the concepts of fundamental protocols
4. To understand the network traffic, congestion, controlling
and resource allocation.
Detailed Syllabus:
UNIT I 8 Hours
Foundation
Building a Network, Requirements, Perspectives, Scalable Connectivity, Cost-
Effective Resource sharing, Support for Common Services,
Manageability,Performance, Bandwidth and Latency, Delay X Bandwidth Product,
Perspectives on Connecting, Classes of Links, Reliable Transmission, Stop-and-
Wait, Sliding Window, and Concurrent Logical Channels.
Self Study: Protocol layering 2 Hours
Unit II 8 Hours
Internetworking-I
Switching and Bridging, Datagram’s, Virtual Circuit Switching, Basic
Internetworking (IP), What is an Internetwork ?, Service Model, Global Addresses,
Datagram Forwarding in IP, sub netting and classless addressing, Address
Translation(ARP), Host Configuration(DHCP), Error Reporting(ICMP), Virtual
Networks and Tunnels.
Self Study: Source Routing 2 Hours
Unit III 8 Hours
Internetworking-II
Network as a Graph, Distance Vector (RIP), Link State (OSPF), Metrics, The Global
Internet, Routing Areas, Routing among Autonomous systems (BGP), IP Version
6(IPv6)
Self Study: Mobilty and Mobile IP 2 Hours
Unit IV 8 Hours
End-to-End Protocols
Simple Demultiplexer (UDP), Reliable Byte Stream(TCP), End-to-End Issues,
Segment Format, Connecting Establishment and Termination, Triggering
Transmission, Adaptive Retransmission, TCP Extensions, Fair Queuing, TCP
Congestion Control, Additive Increase/ Multiplicative Decrease, Slow Start, Fast
Retransmit and Fast Recovery
Self Study: Queuing Disciplines, FIFO 2 Hours
Unit V 8 Hours
Congestion Control and Resource Allocation
Congestion-Avoidance Mechanisms, DEC bit, Random Early Detection (RED),
Source-Based Congestion Avoidance. The Domain Name System(DNS),Electronic
Mail(SMTP,POP,IMAP,MIME),World Wide Web(HTTP),Network
Management(SNMP)
Self Study: Traditional and Multimedia Applications 2 Hours
Course Outcomes:
The students should be able to:
1. List and classify network services, protocols and architectures, explain why they
are layered [L1].
2. Choose key Internet applications and their protocols, and apply to develop their
own applications (e.g. Client Server applications, Web Services) using the sockets
API [L4, L3].
3. Explain develop effective communication mechanisms using techniques like
connection establishment, queuing theory, recovery etc. [L1].
4. Explain various congestion control techniques [L1].
Prerequisite:
1. Knowledge of Computer Networks.
Text books:
1. Larry Peterson and Bruce S Davis “Computer Networks :A System Approach”
5th Edition, Elsevier -2014 2. Douglas E Comer, “Internetworking with TCP/IP, Principles, Protocols and
Architecture” 6th Edition, PHI – 2014
Reference Books:
1. Ulysses Black “Computer Networks, Protocols, Standards and Interfaces”, 2nd Edition-PHI
2. Behrouz A Forouzan “TCP/IP Protocol suite”, 4th Edition – Tata McGraw-Hill
Scheme of Continuous Internal Evaluation (CIE):
The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10
marks each and quiz/course seminar/course project of 10 marks each). The weight-age of
CIE is as shown in the table below.
Component Average of
best 2 Tests
Test-2
Average of 2
Assignments
Quiz/Seminar/
Project
Total
Marks
Maximum marks
Marks
30 10 10 50
Scheme of Semester End Examination (SEE):
Semester end examination will be conducted for 100 marks which will be converted into
50 marks. SEE question paper will have two compulsory questions (any 2 units) and
choice will be given in the remaining three units.
Second Semester
Artificial Intelligence and Agent Technology
Subject Code: SCS23 Credits: 4
Course Type: PC-E CIE Marks: 50
Hours/week: L – T – P 3 – 1 – 0 SEE Marks: 50
Total Hours: 40 SEE Duration: 3 Hours
Course Objectives:
1. To implement non-trivial AI techniques in a relatively large system.
2. To understand uncertainty and problem solving techniques.
3. To understand various symbolic knowledge representation to specify domains
and reasoning tasks of a situated software agent.
4. To understand different logical systems for inference over formal domain
representations and trace how a particular inference algorithm works on a given
problem specification.
5. To understand various learning techniques and agent technology.
Detailed Syallabus:
Unit I 8 Hours
What is Artificial Intelligence: The AI Problems, The Underlying assumption, what
is an AI Technique? The Level of the model, Criteria for success, some general
references, one final word and beyond.
Problems, problem spaces, and search: Defining the problem as a state space
search, Production systems, Problem characteristics, Production system characteristics,
Issues in the design of search programs, Additional Problems.
Self Study: Intelligent Agents: Agents and Environments, The nature of
environments,The structure of agents. . 2 Hours
Text Book 1: Chapter 1 & 2 Text Book 2: Chapter 2
Unit II 8 Hours
Heuristic search techniques: Generate-and-test, Hill climbing, Best-first search,
Problem reduction, Constraint satisfaction, Mean-ends analysis.
Knowledge representation issues: Representations and mappings, Approaches to
knowledge representation, Issues in knowledge representation, The frame problem.
Logical Agents: Knowledge –based agents, the Wumpus world, Logic-
Propositional logic, Propositional theorem proving, Effective propositional model
checking, Agents based on propositional logic.
Self-study : Using predicate logic: Representing simple facts in logic, representing
instance and ISA relationships, Computable functions and predicates, Resolution,
Natural Deduction. 2 Hours
Text Book 1: Chapter 3, 4 & 5 Text Book 2: Chapter
Unit III 8 Hours
Symbolic Reasoning under Uncertainty: Introduction to non-monotonic reasoning,
Logic for non-monotonic reasoning, Implementation Issues, Augmenting a problem-
solver, Implementation:
Statistical Reasoning: Certainty factors and rule-based systems, Bayesian
Networks, Dempster-Shafer Theory.
Quantifying Uncertainty: Acting under uncertainty, Inference using full joint
distributions, Independence, Baye’s rule and its use, The Wumpus world revisited.
Self-Study: Depth-first search, Implementation: Breadth-first search. Basic probability
notation, Probability and Bayes Theorem. 2 Hours
Text Book 1: Chapter 7 & 8 Text Book 2: Chapter
Unit IV 8 Hours
Weak Slot-and-filter structures: Semantic Nets Frames.
Strong slot-and –filler structures: Conceptual dependency, scripts, CYC.
Adversarial Search: Games, Optimal Decision in Games, Alpha-Beta Pruning,
Imperfect Real-Time Decisions, Stochastic Games, Partially Observable Games, State-
Of-The-Art Game Programs, Alternative Approaches, Summary.
Text Book 1: Chapter 9 & 10 Text Book 2: Chapter 5
Unit V 7 Hours
Learning From examples: Learning decision trees, Classification with linear models,
KNN. Evaluating and choosing the best hypothesis, The theory of learning ,PAC, ,
Nonparametric models, Support vector machines, Ensemble learning.
Learning Probabilistic Models: Statistical learning, learning with complete data
Text Book 2: Chapter 18 & 20
Self-Study: Forms of learning, Supervised/Unsupervised learning, Reinforced learning,
Regression. 2 Hours
Prerequisite:
1. Discrete Mathematical Structures
2. Probability
Course Outcomes:
The students will be able to
1. Design intelligent agents for problem solving, reasoning, planning, decision
making and learning for specific design and performance constraints and
when needed, design variants of existing algorithms [L4].
2. Apply AI techniques on current applications [L3].
3. Demonstrate ability for problem solving, knowledge representation, reasoning
and learning [L3].
Text Books:
1. Elaine Rich Kevin Knight, Shivashankar B Nair: Artificial Intelligence, Tata
McGraw Hill 3rd edition 2013.
2. Stuart Russel, Peter Norvig: Artiificial Intelligence A Modern Approach, Pearson
3rd edition 2013. Reference Books:
1. Nils J. Nilsson: “Principles of Artificial Intelligence”, Elsevier, ISBN-13: 9780934613101
2. M. Tim Jones, “Artificial Intelligence: A Systems Approach”, Jones and Bartlett Publisher, 2010
Scheme of Continuous Internal Evaluation (CIE):
The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of
10 marks each and quiz/course seminar/course project of 10 marks each). The weight-
age of CIE is as shown in the table below.
Component Average of
best 2 Tests
Test-2
Average of 2
Assignments
Quiz/Seminar/
Project
Total
Marks Maximum marks
Marks
30 10 10 50
Scheme of Semester End Examination (SEE):
Semester end examination will be conducted for 100 marks which will be converted
into 50 marks. SEE question paper will have two compulsory questions (any 2 units)
and choice will be given in the remaining three units.
Second Semester
Machine Learning Techniques
Subject Code: SCS24 Credits: 4
Course Type: PC CIE Marks: 50
Hours/week: L – T – P 4 – 0 – 0 SEE Marks: 50
Total Hours: 50 SEE Duration: 3 Hours
Course Objectives:
To understand the basic concepts of learning and decision trees.
2. To understand the neural networks and genetic algorithms.
3. To understand the Bayesian techniques.To understand the instant
based learning.
4. To understand the analytical learning and reinforced learning
Detailed Syallabus:
Unit I 10 Hours
Introduction, Concept Learning and Decision Trees
Learning Problems – Designing Learning systems, Perspectives and Issues –
Concept Learning – Version Spaces and Candidate Elimination Algorithm –
Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic
Space Search.
Unit II 10 Hours
Neural Networks and Genetic Algorithms
Neural Network Representation – Problems – Perceptrons – Multilayer Networks and
Back Propagation Algorithms – Advanced Topics – Genetic Algorithms – Hypothesis
Space Search – Genetic Programming – Models of Evolution and Learning.
Unit III 10 Hours
Bayesian and Computational Learning
Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum description
length principle – Bayes optimal classifier – Bayesian belief network – EM algorithm –
Probably Learning – sample complexity for Finite and Infinite hypothesis spaces –
Mistake bound model
Unit IV 10 Hours
Instant based Learning and Learning set of rules
K-nearest neighbor learning – Locally weighted regression – radial basis functions – case
based reasoning – sequential covering algorithms – Learning rule sets – Learning first
order rules – Learning sets of first order rules – Induction as inverted deduction –
Inverting
Unit V 10 Hours
Analytical Learning and Reinforced Learning
Perfect Domain Theories – Explanation Based Learning – Inductive-Analytical
Approaches - FOCL Algorithm – Reinforcement Learning – Task – Q-Learning –
Temporal Difference Learning
Course Outcomes:
On Completion of the course, the students will be able to
1. Choose the learning techniques with the basic knowledge [L4].
2. Apply effectively neural networks and genetic algorithms for appropriate
applications [L3].
3. Apply Bayesian techniques and derive effectively learning rules [L3].
4. Choose and differentiate reinforcement and analytical learning techniques [L4].
Prerequisite:
1. Probability and statistics
2. Linear Algebra
3. Basic design and analysis of algorithms principles
Text Book:
1. Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (INDIAN
EDITION), 2013.
Reference Books:
1. Ethem Alpaydin, “Introduction to Machine Learning”, 2nd Edition, PHI Learning Pvt. Ltd., 2013.
2. T Hastie, R. Tibshirani, J.H.Fiedman, “The Elements of statistical learning”, Springer, 1st Edition 2001.
Scheme of Continuous Internal Evaluation (CIE):
The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of
10 marks each and quiz/course seminar/course project of 10 marks each). The weight-
age of CIE is as shown in the table below.
Component Average of
best 2 Tests
Test-2
Average of 2
Assignments
Quiz/Seminar/
Project
Total
Marks Maximum marks
Marks
30 10 10 50
Scheme of Semester End Examination (SEE):
Semester end examination will be conducted for 100 marks which will be converted
into 50 marks. SEE question paper will have two compulsory questions (any 2 units)
and choice will be given in the remaining three units.
Second Semester
Wireless Networks and Mobile Computing
Subject Code: SCS251 Credits: 4
Course Type: PE-B CIE Marks: 50
Hours/week: L – T – P 4 – 0 – 0 SEE Marks: 50
Total Hours: 50 SEE Duration: 3 Hours
Course Objectives
To introduce the concepts of wireless communication.
1. To understand various propagation methods, Channel models, capacity
calculations multiple antennas and multiple user techniques used in the mobile
communication.
2. To understand CDMA, GSM, Mobile IP, Wimax.
3. To understand different Mobile OS
4. To learn various markup languages CDC, CLDC, MIDP; Programming for
CLDC, MIDlet model and security concerns.
Detailed Syllabus:
Unit I 10 Hours
Mobile Computing Architecture: Architecture for Mobile Computing, 3-tier
Architecture, Design Considerations for Mobile Computing. Wireless Networks :
Global Systems for Mobile Communication ( GSM and Short Service Messages
(SMS): GSM Architecture, Entities, Call routing in GSM, PLMN Interface, GSM
Addresses and Identities, Network Aspects in GSM, Mobility Management, GSM
Frequency allocation. Introduction to SMS, SMS Architecture, SM MT, SM MO, SMS
as Information bearer, applications, GPRS and Packet Data Network, GPRS Network
Architecture, GPRS Network Operations, Data Services in GPRS, Applications for
GPRS, Billing and Charging in GPRS, Spread Spectrum technology, IS-95, CDMA
versus GSM, Wireless Data, Third Generation Networks, Applications on 3G,
Introduction to WiMAX.
Unit II 10 Hours
Mobile Client: Moving beyond desktop, Mobile handset overview, Mobile phones
and their features, PDA, Design Constraints in applications for handheld devices.
Mobile IP: Introduction, discovery, Registration, Tunneling, Cellular IP, Mobile IP
with IPv
Unit III 10 Hours
Mobile OS and Computing Environment: Smart Client Architecture, The
Client: User Interface, Data Storage, Performance, Data Synchronization, Messaging.
The Server: Data Synchronization, Enterprise Data Source, Messaging. Mobile
Operating Systems: WinCE, Palm OS, Symbian OS, Linux and Proprietary OS
Client Development: The development process, Need analysis phase, Design
phase, Implementation and Testing phase, Deployment phase, Development Tools,
Device Emulators.
Unit IV 10 Hours
Building, Mobile Internet Applications: Thin client: Architecture, the client,
Middleware, messaging Servers, Processing a Wireless request, Wireless Applications
Protocol (WAP) Overview, Wireless Languages: Markup Languages, HDML, WML,
HTML, cHTML, XHTML, VoiceXML. 10 Hours
Unit V 10 Hours
J2ME: Introduction, CDC, CLDC, MIDP; Programming for CLDC, MIDlet model,
Provisioning, MIDlet life-cycle, Creating new application, MIDlet event handling,
GUI in MIDP, Low level GUI Components, Multimedia APIs; Communication in
MIDP, Security Considerations in MIDP.
Course Outcomes:
The student should be able to:
1. Work on state of art techniques in wireless communication [L3].
2. Explore CDMA, GSM and Mobile OS [L2].
3. Develop programs for CLDC, MIDP let model and security concerns [L3].
Prerequisite:
1. Concept of Computer Networks
Text Books:
1. Ashok Talukder, Roopa Yavagal, Hasan Ahmed: Mobile Computing,
Technology, Applications and Service Creation, 2nd Edition, Tata McGraw Hill,
2010.
2. Martyn Mallik: Mobile and Wireless Design Essentials, Wiley India, 2003
Reference Books:
1. Raj kamal: Mobile Computing, Oxford University Press, 2007.
2. Iti Saha Misra: Wireless Communications and Networks, 3G and Beyond, Tata
McGraw Hill, 2009.
Scheme of Continuous Internal Evaluation (CIE):
The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10
marks each and quiz/course seminar/course project of 10 marks each). The weight-age of CIE
is as shown in the table below.
Component Average of
best 2 Tests
Test-2
Average of 2
Assignments
Quiz/Seminar/
Project
Total
Marks
Maximum marks
Marks
30 10 10 50
Scheme of Semester End Examination (SEE):
Semester end examination will be conducted for 100 marks which will be converted into 50
marks. SEE question paper will have two compulsory questions (any 2 units) and choice will
be given in the remaining three units.
Second Semester
Network Programming & Internet Technologies
Course Objectives:
1. To study the Transport layer.
2. To understand the basic socket address structure.
3. To know the use of socket functions.
4. To understand TCP client/server programming.
5. To study the different Internet technologies.
Detailed Syallabus:
Unit I 10 Hours
Transport Layer: Introduction, The big picture, UDP, TCP, Stream Control transmission
protocol, TCP connection establishment and termination, time-wait state, SCTP Association
establishment and terminations, port numbers, TCP port numbers and concurrent servers,
Buffer size and limitations, std Internet services protocol usage by common Internet
applications.
Unit II 10 Hours
Socket Introduction: Introduction, socket address structures, value-result arguments, Byte
ordering functions, Byte manipulation functions, inet_aton, inet_addr and inet_ntoa
functions, inet_pton and inet_ntop functions, sock_ntop and related functios, readn, written,
and readline functions.
Unit III 10 Hours
Elementary Sockets: Introduction, socket functions, connect function, bind function, listen
function, accept function fork & exec functions, concurrent servers, close function,
getsockname and getpeername functions.
Unit IV 10 Hours
TCP Client/Server Examples: Introduction, TCP Echo Server: main function, TCP Echo
Server:str_echo function, TCP Echo Client: main function, TCP Echo Client: str_cli
function, Normal startup, Normal termination, POSIX Signal Handling, SIGHE_D signals,
wait and waitpid functions, connection abort before accept returns, Termination of SIGPIPE
signal, crashing of server Host, crashing and rebooting server that shutdown of server host,
Subject Code: SCS252 Credits: 4
Course Type: PE-B CIE Marks: 50
Hours/week: L – T – P 4 – 0 – 0 SEE Marks: 50
Total Hours: 50 SEE Duration: 3 Hours
summary of TCP example, Data format
Unit V 10 Hours
Internet Technologies: Designing web page, HTML, forms, Dive into Web 2.0, Javascript
objects, XML, PHP.
Course Outcomes:
The students will be able to
1. Write programs to interconnect computers using sockets [L6].
2. Implement the file transfer on TCP client/server architecture [L3].
3. Implement the file transfer on UDP client/server architecture [L3].
4. Design the web pages using Internet technologies like HTML and XML [L6].
Prerequisite:
1. Knowledge of ‘C’ Programming language
Text Books:
1. W. Richard Stevenson, Bill Fenner, Andrew M. Rudoff: Unix Network
Programming, volume 1, Third Edition.
2. Paul Dietel, Harvey Dietel and Abbey Dietel, Internet & World Wide Web: How to
program, Fifth Edition.
Reference Books:
1. Douglas E. Comer, David L. Stevens, “Internetworking with TCP/IP”, Vol. III: Client-
Server Programming and Applications, Linux/Posix Sockets Version: 3, 11 Sep 2000. 2. Chris Bates, “Web Programming, Building Internet applications”, Third Edition,
Wiley publications, 2013.
Scheme of Continuous Internal Evaluation (CIE):
The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10
marks each and quiz/course seminar/course project of 10 marks each). The weight-age of CIE
is as shown in the table below.
Component Average of
best 2 Tests
Test-2
Average of 2
Assignments
Quiz/Seminar/
Project
Total
Marks Maximum marks
Marks
30 10 10 50
Scheme of Semester End Examination (SEE):
Semester end examination will be conducted for 100 marks which will be converted into 50
marks. SEE question paper will have two compulsory questions (any 2 units) and choice will
be given in the remaining three units.
Second Semester
Information Storage Management
Course Objectives:
1. To identify the components of managing the data centre.
2. To understand logical and physical components of a storage infrastructure.
3. To evaluate storage architectures, including storage subsystems SAN, NAS,
IPSAN and CAS.
4. To understand the business continuity, backup and recovery methods.
Detailed Syallabus:
Unit I 10 Hours
Introduction to storage and management
Introduction to Information Storage Management, Data Center Environment, Database
Management System (DBMS), Host Connectivity, Storage, Disk Drive Components,
Intelligent Storage System, Components of an Intelligent Storage System, Storage
Provisioning, Types of Intelligent Storage Systems.
Unit II 10 Hours
Storage networking
Fiber Channel: Overview, SAN and Its Evolution, Components of FC SAN, FC
Connectivity, FC Architecture, IPSAN, FCOE, FCIP, Network, Attached Storage, General-
Purpose Servers versus NAS Devices, Benefits of NAS, File Systems and Network File
Sharing, Components of NAS, NAS I/O Operation, NAS Implementations, NAS File,
Sharing Protocols, Object-Based Storage Devices-Content-Addressed Storage, CAS Use
Cases.
Unit III 10 Hours
Storage networking Backup and Recovery
Business Continuity, Information Availability, BC Terminology, BC Planning Life Cycle,
Failure Analysis, Business Impact Analysis, Backup and Archive, Backup Purpose, Backup
Considerations, Backup Granularity, Recovery Considerations, Backup Methods, Backup
Architecture, Backup and Restore Operations.
Subject Code: SCS253 Credits: 4
Course Type: PE-B CIE Marks: 50
Hours/week: L – T – P 4 – 0 – 0 SEE Marks: 50
Total Hours: 50 SEE Duration: 3 Hours
Unit IV 10 Hours
Cloud Computing
Cloud Enabling Technologies -Characteristics of Cloud Computing -Benefits of Cloud
Computing, Cloud Service Models, Cloud Deployment models, Cloud computing
Infrastructure-Cloud Challenges.
Unit V 10 Hours
Securing and Managing Storage Infracture
Information Security Framework, Storage Security Domains, Security Implementations in
Storage Networking, Monitoring the Storage Infrastructure, Storage Infrastructure
Management Activities, Storage Infrastructure Management challenges.
Course Outcomes:
The students will be able to
1. Distinguish various data storage management systems [L4] .
2. Build storage area networks [L3].
3. Use cloud architecture for managing the data [L4].
4. Ensure security of data centres [L3].
Prerequisite:
1. Knowledge of Storage Area Networks.
Text Books
1. EMC Corporation, “Information Storage and Management”, Wiley India, 2nd Edition,
2011.
Reference Books
1. Robert Spalding, “Storage Networks: The Complete Reference”, Tata McGraw Hill,
Osborne, 2003.
2. Marc Farley, “Building Storage Networks”, Tata McGraw Hill, Osborne, 2nd Edition,
2003. 3. Meeta Gupta, “Storage Area Network Fundamentals”, Pearson Education Limited,
2002.
Scheme of Continuous Internal Evaluation (CIE):
The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10
marks each and quiz/course seminar/course project of 10 marks each). The weight-age of CIE
is as shown in the table below.
Component Average of
best 2 Tests
Test-2
Average of 2
Assignments
Quiz/Seminar/
Project
Total
Marks
Maximum marks
Marks
30 10 10 50
Scheme of Semester End Examination (SEE):
Semester end examination will be conducted for 100 marks which will be converted into 50
marks. SEE question paper will have two compulsory questions (any 2 units) and choice will
be given in the remaining three units.
.
Second Semester
Web Security
Subject Code: SCS254 Credits: 4
Course Type: PE-B CIE Marks: 50
Hours/week: L – T – P 4 – 0 – 0 SEE Marks: 50
Total Hours: 50 SEE Duration: 3 Hours
Course Objectives
1. To understand necessity for securing web applications
2. To know different risks to web applications
3. To take the steps required to mitigate those risks
Detailed Syallabus:
Unit I 10 Hours
Introduction:
The Web Security Landscape, Architecture of the World Wide Web, Cryptography basics,
Cryptography and the web, Understanding SSL and TLS
Digital Identification: Passwords, Biometrics and Digital Signatures.
Unit II 10 Hours
Digital Certificates, CAs and PKI: Web's war on privacy, privacy protecting techniques,
privacy protecting Technologies.
Unit III 10 Hours
Web Server Security:
Physical security for servers, Host security for servers, securing web applications.
Unit IV 10 Hours
Web Server Security:
Deploying SSL server certificates, securing your web service, computer crime
Security for content providers: Controlling access to web content, Client-side digital
certificates, code signing and Microsoft's Authenticode.
Unit V 10 Hours
Security for content providers:
Pornography, Filtering software, Censorship, privacy policies, legislation, P3P, Digital
Payments, Intellectual property and actionable content.
Course Outcomes
The students will be able to
1. To detect and solve common web application security vulnerabilities [L3].
Prerequisite:
1. Knowledge of Network Security.
Text Book
1. Web Security, Privacy and Commerce, Simson Garfinkel, Gene Spafford, 2nd Edition, O’REILLY, 2002
Reference Book:
1. Dafydd Stuttard, “The Web Application Hacker’s Handbook”, Wiley India Pvt. Ltd.
Scheme of Continuous Internal Evaluation (CIE):
The Total marks of CIE shall be 50 (three tests of 30 marks each, two Assignments of 10
marks each and quiz/course seminar/course project of 10 marks each). The weight-age of CIE
is as shown in the table below.
Component Average of
best 2 Tests
Test-2
Average of 2
Assignments
Quiz/Seminar/
Project
Total
Marks Maximum marks
Marks
30 10 10 50
Scheme of Semester End Examination (SEE):
Semester end examination will be conducted for 100 marks which will be converted into 50
marks. SEE question paper will have two compulsory questions (any 2 units) and choice will
be given in the remaining three units.
Second Semester
Machine Learning Laboratory
Subject Code: SCS26 Credits: 2
Course Type: PC CIE Marks: 25
Hours/week: L – T – P 0 – 0 – 2 SEE Marks: 25
Total Hours: 40 SEE Duration: 3 Hours
Course Objectives:
1. To acquire a basic knowledge about the key algorithms and theory that forms the
foundation of machine learning and computational intelligence.
2. To achieve a practical knowledge of machine learning algorithms and methods.
LABORATORY WORK
(The following tasks can be implemented in a language of your choice or any tools
available)
1) Show how the CANDIDATE – ELIMINATION algorithm. Show how it is used
to learn from training examples and hypothesize new instances in Version Space.
2) Show how the FIND–S algorithm. Show how it can be used to classify new instances
of target concepts. Run the experiments to deduce instances and hypothesis consistently.
3) Demonstrate the use of ID3 algorithm for learning Boolean-valued functions for
classifying the training examples by searching through the space of a decision tree.
4) Demonstrate the back-propagation algorithm for learning the task of classification
involving applications like face-recognition.
5) Show the application of Naïve Bayes alogorithm for learning and classifying text
documents.
6) Demonstrate the use K-nearest neighbor algorithm for unsupervised learning task with the
help of a suitable example.
7) Show the use of support-vector machine for a linear classification problem of suitable
application.
The students are required to carry out a mini project based on the topics that they have
learnt.
Course Outcomes:
On Completion of the course, the students will be able to
1. Demonstrate the the use of machine learning tools such as Weka ,R and matlab.[L2]
2. Identify and apply the appropriate machine learning technique to classification,
pattern recognition, optimization and decision problems [L1,L3].
Scheme of Continuous Internal Evaluation (CIE):
The total marks of CIE shall be 25. The weight-age of CIE is as shown in the table below.
Component Lab
Attendance
Lab Journal Internal Lab
test
Total Marks
Max. Marks 5 10 10 25
Scheme of semester-end examination (SEE):
Semester end examination will be conducted for 50 marks which will be converted to into 25
marks. The students have to execute one of the given lists of experiments for 25 marks and
demonstrate the working of the mini-project for another 25 marks.
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