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Detailed Syllabus Detailed Syllabus Subject Code 14M1NCI334 Semester odd Semester Sixth Session 2018- 2019 Month from July to December Subject Name Web Algorithms Credits 3 Contact Hours 3 Faculty Coordinator(s) 1. Anuja Arora (Names) Teacher(s) 1. Anuja Arora 2. Neetu Sardana (Alphabetically) SNO Description Cognitive Level (Bloom Taxonomy) C121.1 Outline web caching strategies at varied level-user, web server, and Understand Level gateway server (Level 2) C121.2 Implement and evaluate various recommendation algorithm, link Evaluate Level prediction algorithms, and News Feed Algorithms to provide solutions (Level 5) for various social media and E-Commerce Applications. C121.3 Apply Statistical techniques like Newman Girvan, K-Lin, CPM, Max- Apply Level Min cut, etc to discover web based communities. (Level 3) C121.4 Apply Link Analysis using Page Rank, HITS, and link structure of Analysis Level web using Power, Zipf, or Pareto Law. (Level 4) C121.5 Design information Diffusion on web using information cascade Create Level (Level model, linear threshold model, epidemic models etc. 6) Module No. Subtitle of the Module Topics in the module No. of Lectures for the module 1. Searching , crawling and a. Link Based Search Algorithm, Web 5 indexing Algorithms Crawling, Indexing, Searchiing, Zone Indexing, Term-Frequency, Link Analysis Algorithm, Page rank vector , alpha and power method, user clicks, Naive bayes classifier, hybrid approaches, Doc rank. b. Website performance optimization 2. Google Patent Algorithm Page Rank Algorithm, Random Surfer 5 Algorithm, Pigon rank Algorithm, Hilltop Algorithm, Topic Sensitive Page Rank algorithm, Google Panda, Google penguin and google Hummingbird Algorithm, Facebook Algorithms Spamdexing, Author rank News feed Algorithm(NFO), Edge Rank Algorithm. JIIT University, Noida 3 Web caching Algorithm 3 LRV, FIFO, LRU, Random, OPT 4 Web Ontology Semantic Modeling, Resource 3 description Language(RDF), Web Ontology Language(OWL), ontology query, ontology rules 5 Recommendation Collaborative Filtering, Item-to-Item 4 Algorithms recommendation, Memory Based Recommendation, 6 Graph Theory and Web Directed and Undirected graphs, 4 Connectivity, Component, Path, diameter, Geodesic path, Giant Component, SCC, WCC, bipartite graphs, Clique, Subgraphs. Network Measurements, Graph Structure of Web 7 Evolution of Social Network Random network: Erdos-Renyi and 4 Barabasi-Albert, Small World Phenomenon, Power Law, Ziff Law, Pareto Principle, 8 Community detection Community Discovery Algorithms: 5 Newmann Girvan Algorithm, K-Lin Algorithm, Ford Fulkerman Algorithm, CPM. 9 Game Theory Nash equilibrium, Pareto optimality, 2 Social Optimality. 10 Centrality Degree centrality, Betweenness 2 centrality, Closeness Centrality, Eigen vector centrality. Prestige, Proximity Prestige, Rank Prestige, Co-citation, Bibliographic coupling. 11 Information Diffusion Stability Analysis, Dynamic Analysis, 4 Decision Based Models of Cascades, Probabilistic Models of Information Flow, Epidemic Models. Total number of Lectures 41 Recommended Reading material: Author(s), Title, Edition, Publisher, Year of Publication etc. ( Text books, Reference Books, Journals, Reports, Websites etc. in the IEEE format) 1. Liu, Bing. Web data mining. Springer-Verlag Berlin Heidelberg, 2007. 2. Chakrabarti, Soumen. Mining the Web: Discovering knowledge from hypertext data. Morgan Kaufmann, 2003. JIIT University, Noida 3. Scime, Anthony, ed. Web mining: applications and techniques. IGI Global, 2005. 4. Hitzler, Pascal, Markus Krotzsch, and Sebastian Rudolph. Foundations of semantic web technologies. CRC Press, 2011. 5. King, Andrew B. Website optimization. " O'Reilly Media, Inc.", 2008. 6. Segaran, Toby. Programming collective intelligence: building smart web 2.0 applications. " O'Reilly Media, Inc.", 2007. 7. Charu.C. Aggarwal, Social Network Data Analytics, Springer Science+Business Media, LLC 2011 8. Easley, David, Jon Kleinberg. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. New York, NY: Cambridge University Press, 2010. 9. Jackson, Matthew O. Social and Economic Networks. Princeton, NJ: Princeton University Press, 2008 JIIT University, Noida Detailed Syllabus Lecture-wise Breakup Subject Code 17M1NCI131 Semester Odd Semester – 2nd (specify Odd/Even) Month from Jul to Dec Subject Name Flexible Computer Networks Credits 3 Contact Hours 3 Faculty Coordinator(s) 1. Sangeeta Mittal (Names) Teacher(s) 1. Sangeeta Mittal (Alphabetically) Course Outcomes CO# Course Outcome Cognitive Level (Bloom’s Taxonomy) 1. Explain the current network-traffic characteristics and modern networking scenarios Understanding (level - 2) 2. Assess limitations of classical networking techniques in supporting recent applications Analyzing (level-4) 3. Explain Software Defined Network architecture, need and concepts Understanding (level - 2) 4. Experiment with Openflow based southbound API in Mininet emulator Applying(level-3) 5. Evaluate SDN using Pox and OpenDaylight SDN Controllers Evaluating(level-5) 6. Build traffic engineering modules for load balancing, quality of service and multicast Creating(level-6) data transport in SDN Module No. Subtitle of the Module Topics in the module No. of Lectures for the module 1. Modern Networking Elements Fast Ethernet , Gigabit WiFi, 4G/5G 3 Cellular , Cloud Computing , IoT 2. Basics of Modern Network Types of Network Traffic, Real time 4 Traffic characteristics, Big Data, Cloud Computing and Mobile Traffic , QoS and QoE – Difficulties in achieving them 3. Drivers and Components of Evolving Requirements 2 Flexible Networking SDN and NFV 4. Introduction to Software Architecture , Characteristics, 3 Defined Network (SDN) Standards, Open Development Initiatives 5. SDN Data Plane and Open Flow Data Plane Functions, OpenFlow logical 6 network Device – Flow Tables, Group Tables, Openflow Protocol 6. SDN Control Plane Control Plane Architecture , 6 OpenDaylight Project – Architecture and APIs 7. SDN Application Plane Application Plane Architecture, Data 6 center networking and Information center networking over SDN 8. Network Function Virtualization Approach, NFV use 4 JIIT University, Noida Virtualization (NFV) – cases, NFV and SDN Concepts 9. NFV Infrastructure Virtualized Network Functions, Virtual 6 LAN, Virtual Tenant Network Total number of Lectures 40 JIIT University, Noida Detailed Syllabus Lecture-wise Breakup Subject Code 17M11CS112 Semester Odd Semester Even Session 2018 - 19 (specify Odd/Even) Month from July to December Subject Name Machine Learning and Data Mining Credits 3 Contact Hours 3 Faculty Coordinator(s) Bharat Gupta (Names) Teacher(s) Bharat Gupta COURSE OUTCOMES COGNITIVE LEVELS Differentiate between Classification, Clustering and Association Rules C2 C112.1 techniques. Apply and Compare different classification techniques, e.g., k-Nearest C3 C112.2 Neighbours, Naïve Bayes, ID3 Decision Trees, Support Vector Machine, Ensemble methods , etc. Apply and compare different clustering techniques, e.g., k-means, k- C3 C112.3 mediods, etc. Apply Apriori algorithm to generate the frequently used rules in a C3 C112.4 market basket analysis. Apply different dimensionality reduction techniques e.g. PCA, SVD, C3 C112.5 Factor Analysis, Linear Discriminant Analysis, etc., in big data scenarios. Use Artificial Neural Network techniques, i.e., Back propagation, Feed C3 forward Network, Kohonen Self-Organising Feature Maps, Learning C112.6 Vector Quantization, etc, for solving classification and clustering problems. Module Subtitle of the Topics in the module No. of Lectures No. Module for the module 1 Introduction Introduction to Machine Learning, Data Mining and 2 Knowledge Discovery in Data Bases, Data Types 2 Classification Introduction to classification, k-Nearest Neighbours, Naïve 6 Bayes, Decision Trees 3 Regression Linear Regression with One Variable, Linear Regression 4 with Multiple Variables, Logistic Regression 4. Clustering Introduction, Different type of Clustering Methods, 6 Partitioning Clustering Methods, Hierarchical Clustering Methods, k-means, k-medoids 5. Association Rules Frequent itemsets, Apriori algorithm, Association rules 4 Machine Learning and Data Mining (17M11CS112) 6. Dimensionality Introduction, Subset Selection, PCA, SVD, Factor Analysis, 8 Multidimensional Scaling, Linear Discriminant Analysis Reduction 7. Artificial Neural Cost Function, Back propagation, Feed forward Network, 8 Network training, Error Propagation, Application of Neural Methods Networks 8. Ensemble Methods Ensemble methods of classification-Bagging, Boosting, and 4 Random Forest Total number of Lectures 42 Evaluation Criteria Components Maximum Marks T1 20 T2 20 End Semester Examination 35 TA 25 (Attendance (10), Quiz performance (15)) Total 100 Recommended Reading material: Author(s), Title, Edition, Publisher, Year of Publication etc. ( Text books, Reference Books, Journals, Reports, Websites etc. ) 1. Jiawei Han, Micheline Kamber, Data Mining, Morgan Kaufmann Publishers,Elsevier,2005 2. Kimball R. and Ross M ,The Data Warehouse Toolkit”, Wiley 3. Pujari, Arun K,Data mining and statistical analysis using SQL, Universities press 4. Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Introduction to Data Mining 5. Soumen Chakrabarti, Mining the Web: Discovering knowledge from hypertext data”, Morgan Kaufmann, Elsevier 6. Alex, Berson,Stephen J.Smith, Data Warehousing, data mining and OLAP , McGraw-Hill,2004 7. Inmon W.H.,Building the Data Warehouse ,4th Edition, Wiley 8. Anahory S. and Murray D, Data Warehousing in the Real
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