Introduction to Data analysis: statistical modeling, total information awareness, Bonferroni's Principle; Distributed File systems: MapReduce and Spark; Dimensionality Reduction: PCA, SVD, IGA; Finding Similar Items: Distance Measures, Near Neighbour Search, Similarity based search algorithms; Mining Data Streams: Stream Data Model, Sampling, filtering, Counting distinct elements; Link Analysis: Hubs and Authorities, PageRank, Personalized PageRank; Recommendation Systems: Content based, Collaborative filtering, Hybrid models, Evaluating recommender systems; Mining Social-network Graphs: graph centrality concepts, clustering, community detection, partitioning, overlapping community detection, SimRank; Applications of Large-scale Machine Learning, Current trends and research.