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Course Detail

Course Name Machine Learning for Big Data
Course Code 18CS701
Credits Coimbatore
Year Taught 2018


Course Syllabus

Concept of Machine Learning: Approaches to Modelling – Importance of Words in Documents – Hash Functions- Indexes – Secondary Storage -The Base of Natural Logarithms – Power Laws – MapReduce. Finding similar items: Shingling – LSH – Distance Measures. Mining Data Streams: Stream data model – Sampling data – Filtering streams. Link Analysis: Page Rank, Link Spam.

Frequent Item Sets: Market Basket Analysis, A-Priori Algorithm – PCY Algorithm, Clustering: Hierarchical clustering, K-Means, Clustering in Non-Euclidean Spaces, BFR, CURE. Recommendation Systems: Utility matrix – Content based – Collaborative filtering – UV Decomposition. Mining Social Network Graphs: Social networks as graphs–Clustering – Partitioning – Simrank. Dimensionality Reduction: Eigen Value Decomposition- PCA – SVD.

Large Scale Machine Learning: Neural Networks – The Support Vector Machines model and use of Kernels to produce separable data and non-linear classification boundaries. Overview – Deep learning; Tools for Data Ingestion; analytics and visualization.

Text Books

  1. Anand Rajaraman, Jure Leskovec and J.D. Ullman, “Mining of Massive Data Sets”, ebook, Cambridge University Press, 2014.
  2. Kevin P. Murphey, “Machine Learning, a Probabilistic Perspective”, The MIT Press Cambridge, Massachusetts, 2012,
  3. Tom M. Mitchel, “Machine Learning”, McGraw Hill, 2013.


‘Machine Learning for Big Data’ is an elective course offered in M. Tech. in Computer Science and Engineering at School of Engineering, Amrita Vishwa Vidyapeetham.

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