Week 1 : Background: Introduction (30 mins) Probability: Concentration inequalities, (30 mins) Linear algebra: PCA, SVD (30 mins) Optimization: Basics, Convex, GD. (30 mins) Machine Learning: Supervised, generalization, feature learning, clustering. (30 mins)
Week 2 : Memory-efficient data structures: Hash functions, universal / perfect hash families (30 min) Bloom filters (30 mins) Sketches for distinct count (1 hr) Misra-Gries sketch. (30 min)
Week 3 : Memory-efficient data structures (contd.): Count Sketch, Count-Min Sketch (1 hr) Approximate near neighbors search: Introduction, kd-trees etc (30 mins) LSH families, MinHash for Jaccard, SimHash for L2 (1 hr)
Week 4 : Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (1.5 hr) Randomized Numerical Linear Algebra Random projection (1 hr)
Week 5 : Randomized Numerical Linear Algebra CUR Decomposition (1 hr) Sparse RP, Subspace RP, Kitchen Sink (1.5 hr)
Week 6 : Map-reduce and related paradigms Map reduce - Programming examples - (page rank, k-means, matrix multiplication) (1 hr) Big data: computation goes to data. + Hadoop ecosystem (1.5 hrs)
Week 7 : Map-reduce and related paradigms (Contd.) Scala + Spark (1 hr) Distributed Machine Learning and Optimization: Introduction (30 mins) SGD + Proof (1 hr)
Week 8 : Distributed Machine Learning and Optimization: ADMM + applications (1 hr) Clustering (1 hr) Conclusion (30 mins)
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