Course Description

Course Information


Week Session Topics Resources Assignments
Week-1 Session-1 Data Matrix, Attributes, Vector Recap, Basic Statistics, Distributions, PDF, CDF
Book: DMML, Chapter 1 and Chapter 2
Week-2 Session-2 Multivariate Gaussian, Covariance Matrix, Geometry of the multivariate normal, Diagonalization of Covariance Matrix
Book: DMML, Chapter 1 and Chapter 2
Week-3 Session-3 Frequent Itemset Mining, The Market-Basket Model, Mining Association Rules, Finding Frequent Pairs, A-Priori Algorithm, FP Growth, *Eclat algorithm
Book: MMDS, Chapter 6
Week-4 Session-4 Mining Data Streams, General Stream Processing Model, Sampling from a Data Stream, *Queries over a (long) Sliding Window
Book: MMDS, Chapter 4
Week-5 Session-5 Analysis of Large Graphs: Link Analysis, PageRank, Topic Specific Page rank, *Sim Rank
Book: MMDS, Chapter 5
Week-6 Session-6 Recommender Systems, Content-based Systems, Collaborative Filtering>Book: MMDS, Chapter 9
Week-7 Session-7 Recommender Systems, Latent Factor Models, SVD
Book: MMDS, Chapter 9, 11
Week-8 Session-8 Application of SVD in recommender system, *SVD for dimension reduction
Book: MMDS, Chapter 9, 11
Week-9 Session-9 Analysis of Large Graphs: Community Detection, Betweenness, Modularity, Graph Partitioning, *Graph Cut, Spectral Partitioning
Book: MMDS, Chapter 10
Week-10 Session-10 Map-Reduce and the New Software Stack
Book: MMDS, Chapter 10
Week-11 Session-11 *Finding Similar Items: Locality Sensitive Hashing, *Distance Measure, *MinHashing
Book: MMDS, Chapter 3
Week-12 Session-12 TBA TBA