IT350
Course Name:
Data Analytics (IT350)
Programme:
B.Tech (IT)
Semester:
Sixth
Category:
Programme Core (PC)
Credits (L-T-P):
(3-0-2) 4
Content:
Introduction to Data analysis: statistical modelling, total information awareness, Bonferroni's Principle; Distributed File systems: MapReduce and Spark; Dimensionality Reduction: PCA, SVD; Finding Similar Items: Distance Measures, Near Neighbour Search; Mining Data Streams; Link Analysis, Mining Social-Network Graphs: graph centrality concepts, clustering, community detection, partitioning, overlapping community detection, SimRank; Applications of Large-scale Machine Learning, Neural Network Models like Multi-Layer Perceptron (MLP), Recurrent Neural Networks (RNN), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM).
References:
Josh Patterson and Adam Gibson, "Deep learning: A Practitioner's Approach", O'Reilly, 2017
Ian Goodfellow, Y. Bengio and A. Courville, "Deep Learning", MIT Press, 2016.
Michael A. Nielsen, "Neural Networks and Deep Learning", Determination Press, 2015
Li Deng and Dong Yu, "Deep Learning: Methods and Applications", 2013
Koller, D. and Friedman, N. Probabilistic Graphical Models . MIT Press. 2009
Hastie, Trevor, et al. The elements of statistical learning. Vol. 2. No. 1. New York: springer, 2009
Jure Leskovec et al., "Mining of Massive Datasets" Cambridge University Press, 2014
Tom White “ Hadoop: The Definitive Guide” Fourth Edition, O’reily Media, 2015.
Department:
Information Technology