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B.E Artificial Intelligence and Machine Learning Big Data Analytics Syllabus - Mumbai University 2026

This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

Big-Data-Analytics.pdf
Semester 7 · Fourth Year AI-ML · 3 credits · 100 marks

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Syllabus for Big Data Analytics

Semester 7 · Fourth Year AI-ML · 3 credits · 100 marks

Hadoop HDFS and Map Reduce

  • 2.1 Distributed File Systems: Physical Organization of Compute Nodes, Large-Scale File-System Organization. 2.2 MapReduce: The Map Tasks, Grouping by Key, The Reduce Tasks, Combiners, Details of MapReduce Execution, Coping With Node Failures. 2.3 Algorithms Using MapReduce: Matrix-Vector Multiplication by MapReduce, Relational-Algebra Operations, Computing Selections by MapReduce, Computing Projections by MapReduce, Union, Intersection, and Difference by MapReduce 2.4 Hadoop Limitations s. NoSQL
  • 3.1 Introduction to NoSQL, NoSQL Business Drivers, 3.2 NoSQL Data Architecture Patterns: Key-value stores, Graph stores, Column family (Bigtable)stores, Document stores, Variations of NoSQL architectural patterns, NoSQL Case Study 3.3 NoSQL solution for big data, Understanding the types of big data problems; Analyzing big data with a shared-nothing architecture; Choosing distribution models: master-slave versus peer-to-peer; NoSQL systems to handle big data problems. peer-to-peer; Four ways that NoSQL systems handle big data problems Mining Data Streams
  • 4.1 The Stream Data Model: A Data-Stream-Management System, Examples of Stream Sources, Stream Queries, Issues in Stream Processing. 4.2 Sampling Data techniques in a Stream 4.3 Filtering Streams: Bloom Filter with Analysis. 4.4 Counting Distinct Elements in a Stream, Count-Distinct Problem, Flajolet-Martin Algorithm, Combining Estimates, Space Requirements 4.5 Counting Frequent Items in a Stream, Sampling Methods for Streams, Frequent Itemsets in Decaying Windows. 4.6 Counting Ones in a Window: The Cost of Exact Counts, The Datar-Gionis-Indyk-Motwani Algorithm, Query Answering in the DGIM Algorithm, Decaying Windows. Finding Similar Items and Clustering
  • 5.1 Distance Measures: Definition of a Distance Measure, Euclidean Distances, Jaccard Distance, Cosine Distance, Edit Distance, Hamming Distance. 5.2 CURE Algorithm, Stream-Computing , A Stream-Clustering Algorithm, Initializing & Merging Buckets, Answering Queries. Real-Time Big Data Models
  • 6.1 PageRank Overview, Efficient computation of PageRank: PageRank Iteration Using MapReduce, Use of Combiners to Consolidate the Result Vector. 6.2 A Model for Recommendation Systems, Content-Based Recommendations, Collaborative Filtering. 6.3 Social Networks as Graphs, Clustering of Social-Network Graphs, Direct Discovery of Communities in a social graph.

Textbooks

  • 1 Anand Rajaraman and Jeff Ullman ―Mining of Massive Datasetsǁ, Cambridge University Press,
  • 2 Alex Holmes ―Hadoop in Practiceǁ, Manning Press, Dreamtech Press.
  • 3 Dan Mcary and Ann Kelly ―Making Sense of NoSQLǁ – A guide for managers and the rest of us, Manning Press.
  • 1 Bill Franks , ―Taming The Big Data Tidal Wave: Finding Opportunities In Huge Data Streams With Advanced Analyticsǁ, Wiley
  • 2 Chuck Lam, ―Hadoop in Actionǁ, Dreamtech Press
  • 3 Jared Dean, ―Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitionersǁ, Wiley India Private Limited, 2014.
  • 4 Jiawei Han and Micheline Kamber, ―Data Mining: Concepts and Techniquesǁ, Morgan Kaufmann Publishers, 3rd ed, 2010.
  • 5 Lior Rokach and Oded Maimon, ―Data Mining and Knowledge Discovery Handbookǁ, Springer, 2nd edition, 2010.
  • 6 Ronen Feldman and James Sanger, ―The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Dataǁ, Cambridge University Press, 2006.
  • 7 Vojislav Kecman, ―Learning and Soft Computingǁ, MIT Press, 2010

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
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