Scheme & Syllabus - IKGPTU Campusmaincampus.ptu.ac.in/.../2017/02/M.-Tech.-CSE-Syllabus.pdfI. K....

Computer Science & Engineering Scheme & Syllabus

Of M. Tech.

I. K. Gujral Punjab Technical University Jalandhar Jalandhar-Kapurthala Highway Kapurthala 144603, Punjab

I. K. Gujral Punjab Technical University, Kapurthala

Scheme & Syllabus (CSE) Batch 2014 & Onwards

INDEX

Sr. No. Subject Page No.

1. Scheme all semesters 01 - 02

2. Syllabus 1st Semester 03 – 07

3. Syllabus 2nd Semester 08 – 13

4. Syllabus 3rd Semester 14 – 15

5. Syllabus 4th Semester 16

Sr. No. Elective Subject Page No.

1. Large-Scale Management Technique (Elective 2) 17

2. Machine Learning 18

3. Big Data Analytics 19

4. Data Visualization 20

5. Linux Programming (Elective - I) 21 – 22


Scheme & Syllabus (CSE) Batch 2015 & Onwards Page 1 of 22

FIRST SEMESTER Contact Hours: 30 Hrs.

Course Code

Course Title Load Allocation

Marks Distribution Total Marks

Credits

L T P Internal External CSB-202 Higher Mathematics 3 - - 40 60 100 3

CSE-203 University Elective 2 - - 40 60 100 2 CSB-204 Advanced Algorithm Analysis 3 - - 40 60 100 3 CSB-208 Distributed Operating System 3 - 2 50 50 100 4 CSB-209 Advanced Computer Networks 3 - 2 50 50 100 4

CSBE-218 Elective – 1 3 - 2 50 50 100 4 TOTAL 17 - 6 270 330 600 20

SECOND SEMESTER Contact Hours: 30 Hrs.

Course Code


Marks Distribution

Total Marks

Credits

L T P Internal External CSE-205 Advanced Computer

Architecture 3 - 2 50 50 100 4

CSB-206 Advanced Database System 3 - 2 50 50 100 4 CSB-207 Web Services 3 - 2 50 50 100 4 CSE-210 Advanced English 2 - - 40 60 100 2 CSB-211 Fundamental of Big Data

Analytics 3 - - 40 60 100 3

CSB-212 Fundamental of Cloud Computing 3 - - 40 60 100 3

CSB-213 Seminar - - - 40 60 100 2 TOTAL 17 - 06 310 390 700 22

THIRD SEMESTER Contact Hours: 29 Hrs.

Course Code



Credits

L T P Internal External

CSB-214 Dissertation Part-I - - - 40 60 100 8 CSB-215 Information Retrieve & Data

Mining 3 - 2 50 50 100 4

CSBE-216 Elective - 2 3 - 2 50 50 100 4 TOTAL 6 - 4 140 160 300 16



FOURTH SEMESTER Contact Hours: 30 Hrs.

Course Code



Credits


CSB-214 Dissertation Part-II - - - 40 60 100 10

Elective-3 3 - 2 50 50 100 4

Elective-4 3 - 2 50 50 100 4

TOTAL 6 - 4 140 160 300 18

PROGRAMME ELECTIVE Contact Hours: 29 Hrs. Course Code



Credits


CSBE-216 Large-Scale Management Technique (Elective 2)

3 - 2 50 50 100 4

CSBE-217 Machine Learning 3 - 2 50 50 100 3 CSBE-219 Big Data Analytics 3 - 2 50 50 100 4 CSBE-220 Data Visualization 3 - 2 50 50 100 4 CSBE-2018 Linux Programming

(Elective - I) 3 - 2 50 50 100 4

TOTAL 15 - 10 250 250 500 19

UC 05

PC Offered 53

PE Needed 16

UE Needed 02

Total credits Offered (UC+PC+PE)

76

UC – University Core

PC – Programme Core

PE – Programme Elective

UE-University Elective



CSB 202 HIGHER MATHEMATICS

Internal Marks

External Marks

Total Marks

Credits L T P

40 60 100 3 3 - - UNIT I PROBABILITY: Basics of probability, Independence and conditioning on events using Bayes rule, Random variables, Discrete random variables: Bernoulli, Geometric, probability mass functions, conditioning on random variables. Summary statistics: Expectations, variances, moment generating functions, Continuous random variables: Uniform, Exponential, Gaussian, probability density functions, jointly continuous random variables, conditioning on continuous or discrete random variables.

UNIT II SAMPLING THEORY: Sampling Theory, Random samples, Sampling with and without replacement, Sampling distributions, Sampling distributions of means, Sampling distributions of proportions, Sampling distributions of Differences and Sums, Standard Errors.

UNIT III STATISTICAL TECHNIQUES: Regression and correlation –Rank correlation– Partial and multiple correlation- multiple regression, Analysis of correlation and covariance structures, including principal components, factor analysis and canonical correlation, Classification and discrimination techniques, Multivariate inference.

UNIT IV COMBINATORICS: Basics of Counting- The Pigeonhole Principle -Permutations and Combinations -Binomial Coefficients -Generalized Permutations and Combinations -Generating Permutations and Combinations, Recurrence relations.

UNIT V GRAPH THEORY: Introduction, Directed Graphs, Paths and circuits, Trees and fundamental circuits, Cut sets and cut vertices, Matrix representations of Graphs, Incidence matrix – sub matrices – circuit matrix – path matrix – adjacency matrix. Graph Theoretic Algorithms: Connectedness and components – spanning tree – fundamental circuits – cut vertices – directed circuits – shortest path algorithm .

Suggested Readings/ Books: • Ronald E. Walpole , Raymond H. Myers, Sharon L. Myers, Keying E. Ye , Probability and Statistics

for Engineers and Scientists, Pearson, 9th edition (2011) • Kenneth H. Rosen, Discrete Mathematics and its Applications, Random House (1988) • Narasing Deo, Graph theory with application to Engineering and Computer Science, Prentice Hall

India ( 2010 ) • R.A.Johnson, Miller & Freund’s Probability and Statistics for Engineers, seventh edition, Pearson

Education, Delhi (2008). • J.P. Trembley and R.Manohar, “Discrete Mathematical Structures with Applications to Computer

Science”, Tata McGraw Hill – 13th reprint, 2001. • E.M.Reingold, J.Nievergelt, N.Deo, Combinatorial Algorithms: Theory And Practice, Prentice Hall,

N.J (1977)



CSE 203 UNIVERSITY ELECTIVE

Internal Marks

External Marks

Total Marks

Credits L T P

40 60 100 2 2 - -

UNIT I ALGORITHMS COMPLEXITY AND ANALYSIS: Probabilistic Analysis, Amortized Analysis, Competitive Analysis,Internal and External Sorting algorithms: Quick Sort, Heap Sort, Merge Sort, Counting Sort, Bin Sort, Multi-way merge sort, Polyphase sorting, Search: Hashing.

UNIT II ADVANCED DATA STRUCTURES: AVL Trees, Red-Black Trees, Splay Trees, B-trees, Fibonacci heaps, Data Structures for Disjoint Sets, Augmented Data Structures.

UNIT III GRAPHS & ALGORITHMS: Representation, Type of Graphs, Paths and Circuits: Euler Graphs, Hamiltonian Paths & Circuits; Cut-sets, Connectivity and Separability, Planar Graphs, Isomorphism, Graph Coloring, Covering and Partitioning, , Depth- and breadth-first traversals, Minimum Spanning Tree: Prim’s and Kruskal’s algorithms, Shortest-path Algorithms: Dijkstra’s and Floyd’s algorithm, Topological sort, Max flow: Ford-Fulkerson algorithm, max flow – min cut.

UNIT IV STRING MATCHING ALGORITHMS: Suffix arrays, Suffix trees, Rabin-Karp, Knuth-Morris-Pratt, Boyer- Moore algorithm.

Suggested Readings/ Books: • Thomas Coremen, “Introduction to Algorithms”, Third edition, Prentice Hall of India (2009). • Kleinberg J., Tardos E., “Algorithm Design”, 1st Edition, Pearson, 2012. • Motwani R., Raghavan P., “Randomized Algorithms”, Cambridge University Press, 1995. • Vazirani, Vijay V., “Approximation Algorithms”, Springer, 2001.



CSB 204 ADVANCED ALGORITHMIC ANALYSIS

Internal Marks

External Marks

Total Marks

Credits L T P

40 60 100 3 3 - -

OBJECTIVE: To focus on design and analysis of algorithms in various domains that lays foundations for designing efficient algorithms.

EXPECTED OUTCOME: On completion of this course the student would be able to

• Apply the algorithms and design techniques to solve problems; • Have a sense of the complexities of various problems in different domains.

UNIT I INTRODUCTION: Overview of algorithmic design, asymptotic notation and its properties, Growth of Functions, Time complexity and Analysis of algorithms, Recurrence Relations, Amortized analysis.

UNIT II LINEAR PROGRAMMING: Geometry, Farkas' Lemma, Strong Duality, Complexity, Interior-point Algorithms, Ellipsoid Algorithm and Optimization vs. Separation, Extension to Conic Programming.

UNIT III NETWORK FLOWS: Maximum Flows, Min-cost Flows, Cycle Cancelling Algorithms, Strongly Polynomial-time Analysis, Minimum Cuts without Flows.

UNIT IV P AND NP CLASSES: Class P, Polynomial time verification, reducibility, NP-Hard, NP completeness, Cooks theorem, NP-complete problems- Circuitsat, 3Sat-CNF, Clique, vertex-cover and subset sum.

Unit V Approximation Algorithms: Limits to Approximability, Basic Techniques and Vertex Cover, Primal-dual Technique, Set cover problem,Multicommodity Cut via Embedding Metric Spaces, Approximation Scheme for Euclidean TSP.

Suggested Readings/ Books: • Cormen, Leiserson, Rivest and Stein , “Introduction to Algorithms”, 3rd edition,McGraw-Hill, 2009. • E. Horowitz, and S. Sahni, “Fundamentals of Computer Algorithms”, 2nd edition , Computer Science

Press, 2008. • Schrijver, A. “Theory of Linear and Integer Programming” Chichester: John Wiley & Sons, 1998. • Roos, C., T. Terlaky, and J. -Ph. Vial. “Theory and Algorithms for Linear Optimization: An Interior

Point Approach” Chichester: John Wiley & Sons, 1997. • Vazirani, V. “Approximation Algorithms” Berlin: Springer-Verlag, 2001.



CSE 208 DISTRIBUTED OPERATING SYSTEMS

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

OBJECTIVE: To provide the fundamentals for the distributed operating systems that serve foundation for the advanced studies in the area of distributed systems.

EXPECTED OUTCOME: On completion of this course the student would be able to deal distributed operating systems, global clocks, interprocess communication, file and memory management.

UNIT I INTRODUCTION: Fundamental issues in distributed systems, Distributed System Models and Architectures, Classification of Failures in Distributed Systems, Basic Techniques for Handling Faults in Distributed Systems.

UNIT II TIME AND GLOBAL STATES: Logical clocks and physical clocks, events, process states, global states; Distributed Mutual Exclusion, Leader Election, Distributed Deadlock Detection, Remote Procedure Calls, Broadcast Protocols.

UNIT III INTER PROCESS COMMUNICATION: Inter Process Communication and Process Synchronization – Inter Process Communication Ports – Implementation of Port – Port Table Initialization – Port Creation – Sending a Message to Port – Receiving a message from Port – Port Deletion and Reset. Process Synchronization – Classified Synchronized problem – Synchronization solution – Dead lock prevention – Avoidance.

UNIT IV MEMORY AND FILE MANAGEMENT: Memory Management - Introduction – Partitioned Space Allocation – Buffer Pools - Allocation a Buffer – Return a Buffer – Creating a Buffer Pool – Initializing the Buffer Pool Table – Virtual Memory and Memory multiplying Hardware for Demand Paging – Address Translation with a Page Table – Metadata in Page Table entry – Page replacement and Global Clock.

File Management - Operating Systems – Internal and File Management – The Intel Architecture – MS-DOS internal – Windows XP – Internals –UNIX and UNIX internals.

UNIT V DISTRIBUTED OPERATING SYSTEMS: Distributed operating system concept – Architectures of Distributed Systems, Distributed Mutual Exclusion, Distributed Deadlock detection, Agreement protocols, Threads, processor Allocation, Allocation algorithms , Distributed File system design; Real Time Operating Systems: Introduction to Real Time Operating Systems, Concepts of scheduling , Real time Memory Management.

Suggested Readings/ Books: • Davis, Davis William S, “Operating Systems: A Systematic View”, 6th edition, Pearson Education

India, 2007. • Douglas Comer, “Operating System Design: The Xinu Approach, Linksys Version”,2nd edition, CRC

Press, 2011. • Ann McIver McHoes, Ida M. Flynn, “Understanding Operating Systems”, 6th Edition, Cengage

Learning, 2010. • Randy Chow and Theodore Johnson, “Distributed Operating Systems and Algorithms”, Addison-

Wesley, 1997. • G. Coulouris, J. Dollimore, and and T. Kindberg, “Distributed Systems: Concepts and Designs”, 5th

edition, Addison Wesley, 2011. • Mukesh Singhal, and N. G. Shivaratri, “Advanced Concepts in Operating Systems, Distributed,

Database, and Multiprocessor Operating Systems”, 1st edition, McGraw Hill, 1994.



CSB 209 ADVANCED COMPUTER NETWORKS

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

OBJECTIVE: To go beyond the basic level of understanding that is typically offered at an undergraduate networking course.

EXPECTED OUTCOME: On completion of course students will be able to understand the fundamental concepts in routing and addressing, transport protocols and congestion control, emerging distributed applications, and wireless networking.

UNIT I NETWORKING STANDARDS AND SPECIFICATION: Networking standards and specifications, Need for standardization, ISO and the IEEE standards, The IEEE 802 Project

UNIT II OVERVIEW OF OSI AND TCP/IP PROTOCOL SUITE: Layers in the OSI model, TCP/IP protocol suite, Physical layer addressing, Network layer addressing, Client-Server model.

UNIT III ADDRESSING AND ROUTING: IP Addresses: Classful addressing, Subnetting/Supernetting, Classless Addressing, Delivery and routing of IP packets, Interior and Exterior routing.

UNIT IV TCP/IP PROTOCOL SUITE: Socket Interface, Internet Protocol (IP), ICMP and ARP, Transport Layer Protocols -TCP and UDP, Congestion control and Quality of Service, File Transfer protocols - FTP and TFTP, SMTP, SNMP, BOOTP and DHCP, Domain Name System, Mobile IP. Routing protocols - RIP, OSPF and BGP.

UNIT V AD HOC WIRELESS NETWORKS: Cellular and Ad hoc wireless networks, Applications of Ad hoc wireless networks, issues in ad hoc wireless networks, issues in designing a routing protocol for ad hoc wireless networks, Classification of routing protocols, Security in ad hoc wireless networks.

Suggested Readings/ Books: • Behrouz A. Forouzan, “TCP/IP Protocol Suite”, 4th edition, Tata McGraw-Hill, 2010. • W. Richard Stevens, “TCP/IP Illustrated, The Protocols”, 2nd edition, Pearson Education, 2011. • C.Siva Ram Murthy, B.S. Manoj, “Ad hoc Networks-Architectures and protocols”, 3rd edition,

Pearson Education, 2007. • Andrew S. Tenenbaum, “Computer Networks” 4th edition, Prentice Hall, 2011. • D. E. Comer, “Internetworking with TCP/IP Principles, Protocols and Architecture”, Volume - I,

Pearson Education, 2009.



CSB 205 ADVANCED COMPUTER ARCHITECTURE

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

OBJECTIVE: To focus towards the various design options in the area of architecture that lays platform to develop and analyze high performance applications.

EXPECTED OUTCOME: On completion of this course the student would be able to • Identify the need for multi-core architecture for specific applications by developing a suitable

complexity measure. • Identify needs for homogeneous or heterogeneous multi-core architectures for a given

application. • Develop methods to partition a given application program to run on a multi-core processor • Use the Intel multi-core architecture for develop high performance code • Optimize code using appropriate techniques.

UNIT I CONTROL UNIT DESIGN: Overview of IAS Computer, Data path implementation, Register Transfer Notation (RTN), Abstract RTN, Concrete RTN, Control sequence for Simple RISC computer (SRC); Control unit Design, Hardwired control unit Design and Micro programmed control unit Design using control Sequences

UNIT II MEMORY MODULE DESIGN: Conceptual view of memory cell, Memory address map, Memory connections to CPU, Cache memory- Cache memory management techniques, Types of cache’s : Look through, look aside, write through , write around, unified Vs Split, multilevel, cache levels, Cache Misses, performance issues: Mean memory access time, Execution time, Cache Coherence Protocols, Snoopy, MSI, MESI, and MOESI.

UNIT III MULTI-CORE ARCHITECTURE: Parallel computing and why it failed, Multi-processor architecture and its limitations, Need for multi-core architectures, Architecting with multi-cores, Homogenous and heterogeneous cores, Shared recourses, shared busses, and optimal resource sharing strategies. Performance evaluation of multi-core processors, Error management.

UNIT IV MULTITHREADING CONCEPTS: Evolution of Multi-Core Technology, basic concepts of threading and parallel computing, Concurrency, Parallelism,threading design concepts for developing an application, Correctness Concepts: Critical Region, Mutual exclusion, Synchronization, Race Conditions, Performance Concepts: Simple Speedup, Computing Speedup, Efficiency , Granularity , Load Balance, Tools Foundation – Intel® Compiler and Intel® VTune™ Performance Analyzer.

UNIT V MULTI-CORE PROGRAMMING: Introduction to OpenMP , OpenMP Directives, Parallel constructs, Work-sharing constructs, Data environment constructs, Synchronization constructs, Extensive API library for finer control, benchmarking multi-core architecture: Bench marking of processors. Comparison of processor performance for specific application domains.

Suggested Readings/ Books: • John L. Hennessy and David A. Patterson “Quantative Approach –Computer Architecture” 5th

edition, Morgan Kaufmann, 2011. • Shameem Akhter and Jason Roberts, “Multi-Core Programming”, 1st edition, Intel Press, 2006. • Vincent .P. Heuring, Harry F. Jordan “ Computer System design and Architecture” 2nd edition,

Pearson, 2003. • David B. Kirk , Wen-mei W. Hwu, “Programming Massively Parallel Processors: A Hands-on

Approach (Applications of GPU Computing Series)”, 1st edition, Morgan Kaufmann, 2010. • Apman, Gabriele Jost, Ruud van van der Pas, “Using OpenMP: Portable Shared Memory

Parallel Programming (Scientific and Engineering Computation)”, 1st edition, MIT Press, 2007.



CSB 206 ADVANCED DATABASE SYSTEMS

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

OBJECTIVE: To expose the students to the latest industry relevant topics in modern database management systems.

EXPECTED OUTCOME: To enable the students to design their own parallel and distributed databases and to expose to the various warehousing tools.

UNIT I DATABASE DESIGN AND TUNING: Introduction to physical database design – Guideline for index selection- Overview of database tuning – Conceptual schema tuning – Queries and view tuning.

UNIT II PARALLEL AND DISTRIBUTED DATABASE: Parallel database systems: Architecture of parallel databases, parallel Query evaluation, parallelizing joins and parallel - query optimization. Distributed database systems: Distributed database architecture, Properties of distributed database, Types

UNIT III DEDUCTIVE DATABASES: Introduction, Prolog/datalog notation, Interpretation of rules, Basic inference mechanisms for logic programs, Datalog programs and their evaluation, deductive database system, deductive object oriented databases, applications.

UNIT IV DATA WAREHOUSING: Data warehousing: Characteristics of Data warehouse, Data preprocessing, Data warehouse architecture, Multi dimensional data model, Schema design, OLAP Operation and Data mart, Concepts of Data mining.

UNIT V DATABASE TECHNOLOGIES: Object Database Systems, Multimedia databases, Mobile databases, Spatial Database, Temporal database, Data bases on the World Wide Web, Geographic Information system, Genome data management, Digital Libraries.

Suggested Readings/ Books: • Raghu Ramakrishnan and Johannes Gehrke, “Database Management Systems”, 3rd Edition,

McGraw Hill,2007. • S.K.Singh, “Database Systems: Concepts, Design & Applications”, 1st edition, Prentice Hall, 2009. • Ramez Elmasri and B.Navathe, “Fundamentals of database systems”, 4th edition, Addison Wesley,

2008. • Jiawei Han and Micheline Kamber, “Data Mining-Concepts and Techniques”, 2nd edition, Morgan

Kaufmann publishers, 2011.



CSE 207 WEB SERVICES

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

OBJECTIVE: To provide fundamentals on SOA, SOAP UDDI and XML that lays foundations for the advanced studies in the area of web services.

EXPECTED OUTCOME: After completion of this course the students will be perform project in the area of XML.

UNIT I SOA: (SERVICE ORIENTED ARCHITECTURE): Introduction to Services - Bind, Pubish, Find - Framework for SOA – Web Services Architecture, Interoperability – RESTful (Representational State Transfer) Services, WS-Interoperability.

UNIT II XML & WEB SERVICE STANDARDS: Basics of XML –XML standards - SOAP - Messaging, Encoding, Faults, Data types, WS-Routing, WSDL Specification - UDDI Business Registry - UDDI data Models, Types, Inquiry and Publisher APIs.

UNIT III FROM WEB SERVICES TO SEMANTIC WEB SERVICES: Introduction to semantic web services -Resource Description Framework:RDF - Basic elements, Classes and Properties - RDF query, RDF tools, RDF-Semantics.

UNIT IV ONTOLOGY BASICS, WEB ONTOLOGY LANGUAGE: OWL, sub languages- OWL: Lite, DL, Full. Instance, Classes, Properties, DataType Properties, Object Properties, Operators - OWL-S: An upper ontology to describe web services, Building blocks, Validating OWL- S documents.

UNIT V REAL WORLD EXAMPLES & APPLICATIONS: Protégé-OWL, Case Study, Swoogle, Architecture and usage of meta-data, FOAF(Friend Of A Friend), Semantic markup, RSS, feeds, semantic web search engines, Web Crawler, mashups with Examples.

Suggested Readings/ Books: • Sanjiva Weerawarana, Francisco Curbera, Frank Leymann, Tony Storey, Donals F. Ferguson, “Web

Services Platform Architecture: SOAP, WSDL, WS-Policy, WS-Addressing, WS-BPEL, WS-Reliable Messaging and More”, 2nd edition, Prentice Hall PRT, 2005.

• Liyang Yu, “Introduction to the Semantic Web and Semantic Web Services”, 1st edition, Chapman & Hall/CRC, 2007.

• John Hebeler, Matthew Fisher, Ryan Blace, Andrew Perez-Lopez, Mike Dean, “Semantic web programming", 3rd edition, Wiley Publishing Inc, 2009.

• Grigoris Antoniou and Frank van Harmelen, “A Semantic Web Primer”, 2nd edition, MIT Press, 2008.



CSE-210 ADVANCED ENGLISH

Internal Marks

External Marks

Total Marks

Credits L T P

40 60 100 2 2 - -

UNIT I A BRIEF ORIENTATION ON THE THEORY OF BUSINESS COMMUNICATION: Definition of Communication; its nature & process; forms & channels of communication; profile of an Effective Communicator

BUILDING UP AND ENRICHMENT OF VOCABULARY: Learning Derivatives, Prefixes and Suffixes; Homonyms & Homophones; Pairs/Group of words; Synonyms & Antonyms; One word substitution; Foreign words & Phrases.

BASIC SENTENCE FAULTS: Revising and practicing a prescribed set of grammar items; parts of speech, compound & complex sentence constructions, active/ passive, direct/indirect speech, using grammar actively while processing or producing language.

UNIT II APPLICATION OF BUSINESS COMMUNICATION:

a) SPEAKING: Oral communication- Everyday Interactions, Group Discussions, Public speaking; Conversation Skills; Business Etiquette; Presentation Skills-combating stage fright, preparing power point presentation Non-verbal communication in Oral & Power Point Presentations; Telephonic Skills; Preparation for job interview- practice through mock interview.

b) MECHANICS OF WRITING: Descriptive and argumentative essays, Writing business letters, emails; memos, Drafting Reports-training reports, project reports, varied business reports; Scientific & Technical Writing-writing abstracts & summaries, research papers; Career Documents; Preparing a selling resume, covering letters, CVs etc.



CSB 211 FUNDAMENTALS OF BIG DATA ANALYTICS

Internal Marks

External Marks

Total Marks

Credits L T P

40 60 100 3 3 - -

OBJECTIVES: This course provides a broad introduction to big data at a foundation level .It also highlights the state of the practice of data analytics and its Lifecycle to address business challenges that leverage big data. It provides a brief introduction of big data technology and tools, including MapReduce and Hadoop.

EXPECTED OUTCOME: The students will be able to huge volumes of data untapped by the BI programs. They come to know about the Analytics Life Cycle. They get knowledge of open source software framework that supports the processing of large data sets.

UNIT I INTRODUCTION: Big Data Overview - State of the practice in analytics - The role of the Data Scientist - Big Data Analytics in Industry Verticals.

UNIT II ANALYTICS LIFECYCLE: Key roles for a successful analytic project - Main phases of the lifecycle - Developing core deliverables for stakeholders.

UNIT III BIG DATA – TECHNOLOGY AND TOOLS: Introduction to MapReduce/Hadoop for analyzing unstructured data - Hadoop ecosystem of tools - In-database Analytics - MADlib and Advanced SQL Techniques, NoSQL, MDX.

UNIT IV ANALYTICS AND STATISTICAL MODELING FOR BIG DATA – THEORY AND METHODS: Case Study: Big data analytics using -Naïve Bayesian Classifier - K-Means Clustering - Association Rules Decision Trees -Linear and Logistic Regression -Time Series Analysis -Text Analytics.

UNIT V INTRODUCTION TO R: Introduction to R - Analyzing and exploring data with R - Statistics for model building and evaluation.

Suggested Readings/ Books: • Noreen Burlingame ,”Little Book of Big Data” Ed. 2012 • Tom White, “Hadoop , the definitive guide”, O'Reilly Media • Alex Holmes, “Hadoop in practice”, Manning Publications • Donald Miner, “Map Reduce Design Patterns: Building Effective Algorithms and Analytics for

Hadoop and Other Systems, • Nathan Marz , “Big Data: Principles and best practices of scalable real-time data systems”, Manning

Publications • Big Data Now: Current Perspectives, O’Reilly Radar [kindle Edition] • Paul Zikopoulos et al., “Harness the Power of Big Data The IBM Big Data Platform”



CSB 212 FUNDAMENTALS OF CLOUD COMPUTING

Internal Marks

External Marks

Total Marks

Credits L T P

40 60 100 3 3 - -

UNIT I INTRODUCTION: Cloud Computing Overview – Characteristics - challenges, Benefits, limitations – Evolution of IT to Cloud Computing - Cloud computing architecture - Cloud deployment model: Public clouds – Private clouds – Community clouds - Hybrid clouds - Cloud Reference Model – Business Case - Evaluating cloud business impact and economics – Cloud based Solutions for Business applications.

UNIT II SOFTWARE AS A SERVICE (SaaS): Introduction to SaaS – Evolution of SaaS - characteristics - Benefits – Lifecycle – Business model – Application architecture – Multitenant – Different levels of Multitenancy - Evaluating SaaS – Application scalability – SaaS Integration Services – SaaS Integration Products & Platform - Case study – Deploying open-source SaaS application.

UNIT III PLATFORM AS A SERVICE (PaaS): Introduction to PaaS – Characteristics – Benefits – Disadvantages - Types – Service model - Cloud platform & Management Computation – Integrated lifecycle platform – Web application framework - Enabling Technologies as Platform - Emerging Cloud Computing Trends and Innovations – Case study – Deploying application in PaaS engine.

UNIT IV INFRASTRUCTURE AS A SERVICE (IaaS): Introduction to IaaS - Virtualization – Server – Storage – Network – data storage – Local Cloud and Thin Clients - Load balancing – Improving performance through Load balancing - scalability – Managing cloud resource – cloud capacity management – Virtual machine provisioning – Migration service - Case study – Deploying application in IaaS engine.

UNIT V CLOUD SERVICE PROVIDERS: SaaS service providers: Google Docs - Salesforce.com – iCloud - open source software, and commercial cloud service providers. PaaS service providers: Google App Engine - Microsoft Azure – Force.com – Citrix. IaaS service providers: Amazon EC2 - GoGrid – Rackspace – Terremark.

Suggested Readings/ Books: • John Rhoton , “ Cloud Computing Explained : Implementation Handbook for Enterprises”, • Barrie Sosinsky, “Cloud Computing Bible”, Wiley publication. • Kris Jamsa, “Cloud Computing”, Jones & Barlett Learning. • Jothy Rosenberg, Arthur Mateos, “The Cloud At your Service”, Manning Publication, 2011. • Anthony Velte, Toby Velte, “Cloud Computing: A Practical Approach”, McGraw Hill. • Rajkumar Buyya, Cloud Computing Principles and Paradigm, Wiley publication



CSE 215 INFORMATION RETRIEVAL AND DATA MINING

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2 OBJECTIVE: The course is aimed at an entry level study of information retrieval and data mining techniques. It is about how to find relevant information and subsequently extract meaningful patterns out of it. While the basic theories and mathematical models of information retrieval and data mining are covered, the course is primarily focused on practical algorithms of textual document indexing, relevance ranking, web usage mining, text analytics, as well as their performance evaluations. EXPECTED OUTCOME: On completion of this course students are expected to master both the theoretical and practical aspects of information retrieval and data mining. More specifically, the student will understand:

1. The basic concepts and processes of information retrieval systems and data mining techniques. 2. The common algorithms and techniques for information retrieval 3. The quantitative evaluation methods for the IR systems and data mining techniques. 4. The popular probabilistic retrieval methods and ranking principle. 5. The techniques and algorithms existing in practical retrieval and data mining systems such as

those in web search engines and the Amazon book/ Last.FM recommender systems. 6. The challenges and existing techniques for the emerging topics of MapReduce, portfolio retrieval

and online advertising. UNIT I OVERVIEW OF INFORMATION RETRIEVAL AND DATA MINING : Introduction to information retrieval and Data Mining – Data Mining Functionalities, Steps in Data Mining Process – Architecture of a Typical Data Mining Systems . Understand the conceptual models of an information retrieval and knowledge discovery system. Indexing techniques for textual information items- inverted indices, tokenization, stemming and stop words. UNIT II MINING ASSOCIATION RULES: Mining Association Rules in Large Databases, Mining Frequent Patterns - basic concepts - Efficient and scalable frequent item set mining methods, Apriori algorithm, FP-Growth algorithm, Associations - mining various kinds of association rules.

UNIT III PREDICTIVE MODELING AND CLUSTERING: Classification and Prediction-Issues Classification by Decision Tree Induction–Bayesian Classification – Other Classification Methods – Prediction–Clusters Analysis – Basics of cluster analysis -Types of Data in Cluster Analysis – Categorization of Major Clustering Methods – Partitioning Methods – Hierarchical Methods.

UNIT IV RETRIEVAL METHODS AND EVALUATION: Retrieval models- Boolean, Vector space, Binary independence, Language modeling. Probability ranking principle. Other commonly-used techniques include relevance feedback, pseudo relevance feedback, and query expansion. Retrieval Performance Evaluation measures Average precision, NDCG, etc. "Cranfield paradigm.

UNIT V PERSONALISATION AND EMERGING AREAS: Basic techniques for collaborative filtering and recommender systems - memory-based approaches, probabilistic latent semantic analysis (PLSA), and personalized web search- click-through data. Peer-to-peer information retrieval, Learning to Rank Portfolio retrieval and Risk Management.

Suggested Readings/ Books: • Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, “Introduction to Information

Retrieval”, Cambridge University Press. 2008.

• Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, Addison-Wesley, 2006



• Ian H. Witten, Alistair Moffat and Timothy C, “Gigabytes”, Morgan Kaufmann, (2nd Ed.) (1999), San Francisco, California.

• Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer (2006).

• Jiawei Han and Micheline Kambers, “Data Mining –Concepts and Techniques”, 2nd edition, Morgan Kaufman Publications, 2011.

• David Hand, Heikki Mannila and Prdhraic Smyth, “Principles of Data Mining”, 3rd edition, Morgan Kaufman Publications, 2009.

• M. Kantardzic, “Data Mining: Concepts, Models, Methods, and Algorithms”, 2nd edition, Wiley-IEEE Press, 2011.



CSBE 216 LARGE-SCALE DATA MANAGEMENT TECHNIQUES(ELECTIVE –II)

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

OBJECTIVES: Data Management to handle large-scale data arising from Internet and Enterprise -based applications.

EXPECTED OUTCOME: Will be able to develop solutions for both building data-intensive scalable applications over the Internet/Web as well as for large-scale data analytics.

UNIT I DATA MANAGEMENT SOLUTIONS FOR ENTERPRISE APPLICATIONS: Formal Model of Correctness, the Transaction Model, Database Concurrency Control Protocols, Transaction Failures and Recovery, and Database Recovery Protocols.

UNIT II DATA MANAGEMENT SOLUTION FOR INTERNET APPLICATIONS: Google's Application Stack: Chubby Lock Service, Big Table Data Store, and Google File System; Yahoo's key-value store: PNUTS; Amazon's key-value store: Dynamo; Correctness Semantics of key-value store and its impact on application development.

UNIT III DATA ANALYSIS PLATFORMS FOR ENTERPRISE DATA ANALYTICS: Online Analytical Processing, Data Warehouse Architectures, and the Data Cube Model.

UNIT IV LARGE-SCALE DATA ANALYTICS IN THE INTERNET CONTEXT: Governance, Programming paradigms: PigLatin and Hive, and parallel databases versus MapReduce.

UNIT V APPLICATIONS: MASSIVE DATA SETS: Applications: Billing in the Large, Detecting Fraud in the Real World, Massive Datasets in Astronomy, Data Management in Environmental Information Systems, Massive Data Sets issues in Earth Observing, Massive Data Set Issues in Air Pollution Modelling, Mining Biomolecular Data Using Background Knowledge and Artificial Neural Networks.

Suggested Readings/ Books:

• Gerhard WEIKUM and Gottfried VOSSEN, “ Transactional Information Systems: Theory and the practice of concurrency control and recovery”, Morgan Kaufmann Publishers,

• Lars George , “HBase: The Definitive Guide”, O'Reilly Media, Inc. • Even Hewitt , “Cassandra: The Definitive Guide”, O'Reilly Media, Inc • Alex Holmes, “Hadoop in practice”, Manning Publications • James Abello, Panos M. Pardalos, Mauricio G.C. Resende, “Handbook of Massive Data Sets”,

Kluwer Academic Publishers. • Alan Gates , “Programming Pig Dataflow Scripting with Hadoop”, O'Reilly Media, Inc. • Donald Miner, Adam Shook, “MapReduce Design Patterns Building Effective Algorithms and

Analytics for Hadoop and Other Systems”, O'Reilly Media, Inc.



CSBE 217 MACHINE LEARNING

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2 OBJECTIVE: This course provides a broad introduction to machine learning and statistical pattern recognition. Supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory, reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

EXPECTED OUTCOME: The objective of this course is to give students basic knowledge about the key algorithms and theory that form the foundation of machine learning and computational intelligence so that they will be able to understand the principles, advantages, limitations and possible applications of machine learning Identify and apply the appropriate machine learning technique to classification, pattern recognition, optimization and decision making. UNIT I INTRODUCTION: Learning Problems – Perspectives and Issues – Concept Learning – Version Spaces and Candidate Eliminations – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search.

UNIT II SUPERVISED LEARNING: Supervised learning setup, LMS.Logistic regression, Perceptron. Exponential family, Generative learning algorithms. Gaussian discriminant analysis. Support vector machines, Model selection and feature selection, Ensemble methods: Bagging, boosting, Evaluating and debugging learning algorithms.

UNIT III UNSUPERVISED LEARNING: Locally weighted Regression – Radial Bases Functions – Case Based Learning. EM. Mixture of Gaussians. Factor analysis. PCA (Principal components analysis) ICA (Independent components analysis).

UNIT IV BAYESIAN AND COMPUTATIONAL LEARNING: Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum Description Length Principle – Bayes Optimal Classifier – Gibbs Algorithm – Naive Bayes Classifier – Bayesian Belief Network – EM Algorithm – Probability Learning – Sample Complexity – Finite and Infinite Hypothesis Spaces – Mistake Bound Model.

UNIT V ADVANCED LEARNING: Learning Sets of Rules – Sequential Covering Algorithm – Learning Rule Set – First Order Rules – Sets of First Order Rules – Induction on Inverted Deduction – Inverting Resolution – Analytical Learning – Perfect Domain Theories – Explanation Base Learning – FOCL Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning Suggested Readings/ Books: • Introduction to Machine Learning - Ethem Alpaydin, MIT Press, Oct 2004; Prentice Hall of India, 2005 • Tom Mitchell, Machine Learning, McGraw Hill, 1997.

• Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer (2006).

http://www.cmpe.boun.edu.tr/~ethem/i2ml/

http://www.cmpe.boun.edu.tr/~ethem/

http://www.cs.cmu.edu/~tom



CSBE 219 BIG DATA ANALYTICS

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

OBJECTIVE: This course covers tools and techniques required for big data analytics. The course focuses on concepts, principles, and techniques applicable to any technology environment and industry and establishes a baseline that can be enhanced by further formal training and additional real-world experience. EXPECTED OUTCOME

1. Define learning and knowledge analytics 2. Map the developments of technologies and practices that influence learning and knowledge

analytics as well as developments and trends peripheral to the field. 3. Evaluate prominent analytics methods and tools and determine appropriate contexts where the

methods would be most effective. 4. Describe how “big data” and data-driven decision making differ from traditional decision making

and the potential future implications of this transition. 5. Describe and evaluate developing trends in learning and knowledge analytics and develop

models for their potential impact on teaching, learning, and organizational knowledge.

UNIT I OVERVIEW OF HADOOP: Introduction to learning and knowledge analytics- Rise of “Big Data” -Big Data From Technology Perspective- Hadoop- Components of Hadoop-Application Development in hadoop- The Distributed File System -Hadoop Cluster Architecture-Batch Processing-Low Latency NoSQL.

UNIT II MAPREDUCE ALGORITHM DESIGN: MapReduce Basics - Functional Programming Roots - Mappers and Reducers -The Execution Framework - Partitioners and Combiners- MapReduce Algorithm Design- Local Aggregation- Pairs and Stripes- Computing Relative Frequencies - Secondary Sorting- Relational Joins UNIT III REAL TIME ANALYTICS AND SEARCH : In-line queries-In-memory data, data on HDFS, HBase or any other structure on Hadoop clusters. Impala with large scale search engine like SolrCloud. Real-Time Queries in Hadoop

Cloudera Impala: A Modern SQL Engine for Hadoop-scalable parallel database technology available to the Hadoop community

UNIT IV INDEXING FOR TEXT RETRIEVAL : Inverted Indexing for Text Retrieval- Web Crawling- Inverted - Inverted Indexing: Baseline Implementation - Inverted Indexing: Revised Implementation- Index Compression

UNIT V ANALYTICS FOR BIG DATA IN MOTION : Infosphere Stream Basics- How stream works-Streams Processing Language-Stream Tool Kits

Apache Flume NG - Microsoft StreamInsight as tools for complex event processing (CEP) applications. Case Studies Big Data in E-Commerce and IT Energy Consumption, Social and Health Science Suggested Readings/ Books: • Paul C. Zikopoulos,Chris Eaton,Dirk deRoos,Thomas Deutsch ,George Lapis, “Understanding Big

Data: Analytics for Enterprise Class Hadoop and Streaming Data, McGraw-Hill,2012. • Lin and Chris Dyer ,”Data-Intensive Text Processing with MapReduce Jimmy “, Morgan & Claypool

Synthesis,2010. • Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with

Advanced Analytics”, John Wiley& Sons,2012.

http://www.cloudera.com/content/cloudera/en/resources/library/recordedwebinar/impala-real-time-queries-in-hadoop-video-recording.html



CSBE 220 DATA VISUALIZATION

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

OBJECTIVES: This course covers the basic theories of data visualization, such as data types, chart types, visual variables, visualization techniques, color theory, cognitive theory, and data patterns

EXPECTED OUTCOME: Understand the principles of creating and evaluating effective data visualizations

• Can use software tools to create various data visualizations • Familiar with the visualization techniques in major application areas • Acquire the skill to apply visualization techniques to a problem and associated data set

UNIT I PRINCIPLES AND THEORIES OF VISUALIZATION: Theories related to visual information processing - Color theory - Data types - Visual variables -Chart types: statistical graphs, maps, trees and networks

UNIT II ASPECTS OF DATA PATTERNS: Acquisition of data, Discipline-independent classification of information sources, Data base issues – In memory database - storage and retrieval of data - Query languages - Reliability of data – Patterns and predicting data, continuously and discontinuously variable data, plotting data and suitability for different types of data.

UNIT III VISUALIZATION TECHNIQUES: Scalar and point techniques - Vector visualization techniques - Multidimensional techniques – glyphs, Graph-theoretic graphics - Linked Views for Visual Exploration - Multivariate Visualization by Density Estimation, Volume Visualization – Rendering - Attribute Mapping - Visualizing Cluster Analysis - Visualizing Contingency Tables - Matrix Visualization - Visualization in Bayesian Data Analysis - Evaluation of data visualization

UNIT IV APPLICATIONS: Visualization for Genetic Network Reconstruction, Reconstruction, Visualization and Analysis of Medical Images, Exploratory Graphics of a Financial Dataset, Visualization Tools for Insurance Risk Processes, Visualization of Social Networks datasets, Visualizing Darwin’s database – A case study.

UNIT V TOOLS AND LANGUAGES: Programming Statistical Data Visualization in the Java Language, Web-Based Statistical Graphics using XML Technologies, Google Map API, Google Chart, Tableau - Heat Map Generation

Suggested Readings/ Books: • Ben Fry, Visualizing Data: Exploring and Explaining Data with Processing Environment, O'Reilly

Media, 2008 • C.H. Chen, W.K. Hardle, A.Unwin,Handbook of Data Visualization, Springer, Ed(XIV), 2008 • Avril Coghlan, A Little Book of R For Multivariate Analysis, 2013 • Avril Coghlan, A Little Book of R For Biomedical Statistics, 2013 • Paul Murrell, R Graphics, Computer Science and Data Analysis Series • John Verzani, simpler – Using R for Introductory statistics



CBSE 2018 LINUX PROGRAMMING (ELECTIVE –I)

Internal Marks

External Marks

Total Marks

Credits L T P

50 50 100 4 3 - 2

UNIT I INTRODUCTION TO LINUX OPERATING SYSTEM: Introduction and Types of Operating Systems, Linux Operating System, Features, Architecture Of Linux OS and Shell Interface, Linux System Calls, Linux Shared Memory Management, Device and Disk Management in Linux, Swap space and its management. File System and Directory Structure in Linux. Multi-Processing, load sharing and Multi-Threading in Linux, Types of Users in Linux, Capabilities of Super Users and equivalents.

UNIT II INSTALLING LINUX AS A SERVER: Linux and Linux Distributions ;Major differences between various Operating Systems (on the basis of: Single Users vs Multiusers vs Network Users; Separation of the GUI and the Kernel; Domains; Active Directory;).

UNIT III INSTALLING LINUX IN A SERVER CONFIGUARTION : Before Installation; Hardware; Server Design ;Dual-Booting Issues; Modes of Installation; Installing Fedora Linux; Creating a Boot Disk; Starting the Installation; GNOME AND KDE : The History of X Windows; The Downside; Enter GNOME; About GNOME ;Starting X Windows and GNOME; GNOME Basics; The GNOME Configuration Tool.

UNIT IV INSTALLING SOFTWARE: The Fedora Package Manager; Installing a New Package using dpkg and RPM; Querying a Package; Uninstalling a Package using dpkg and RPM; Compiling Software; Getting and Unpacking the Package; Looking for Documentation; Configuring the Package; Compiling Your Package; Installing the Package, Driver Support for various devices in linux.

UNIT V MANAGING USERS: Home Directories ;Passwords; Shells; Stratup Scripts; Mail; User Databases; The / etc /passwd File; The / etc / shadow File; The / etc /group File; User Management Tools; Command-Line User Management; User LinuxConf to Manipulate Users and Groups; SetUID and SetGID Program

UNIT VI THE COMMAND LINE : An Introduction to BASH, KORN, C, A Shell etc. ; BASH commands: Job Control; Environment Variables; Pipes; Redirection; Command-Line Shortcuts; Documentation Tools; The man Command; the text info System; File Listings; Owner ships and permissions; Listing Files; File and Directory Types; Change Ownership; Change Group; Change Mode ; File Management and Manipulation; Process Manipulation; Miscellaneous Tools; Various Editors Available like: Vi and its modes, Pico, Joe and emacs, , Su Command.

UNIT VII BOOTING AND SHUTTING DOWN: LILO and GRUB; Configuring LILO; Additional LILO options; Adding a New Kernel to Boot ; Running LILO; The Steps of Booting; Enabling and disabling Services FILE SYSTEMS: The Makeup File Systems; Managing File Systems; Adding and Partitioning a Disk; Network File Systems; Quota Management; CORE SYSTEM SERVICES: The init Service; The inetd and xinetd Processess; The syslogd Daemon; The cron Program PRINTING: The Basic of lpd; Installing LPRng; Configuring /etc/printcap; The /ETC/lpd.perms File; Clients of lpd, Interfacing Printer through Operating System. Suggested Readings/ Books: • Linux Administration : A Beginner's Guide by Steve Shah , Wale Soyinka, ISBN 0072262591 (0-07-

226259-1), McGraw-Hill Education. • Unix Shell Programming, Yashavant P. Kanetkar. • UNIX Concepts and Applications by Sumitabha Das. • Operating System Concepts 8th edition, by Galvin.

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