Syllabus

The course have 5 different modules as follows:

Module: Introduction

Instructor: Suman Kundu

Randomized algorithms, Universal Hash Family, Probabilistic Algorithm Analysis, Approximation Algorithms, $\epsilon$-Approximation Schemes, Sublinear time complexity, Sublinear Algorithms.

Module: Property Testing

Instructor: Suman Kundu

Testing list’s sortedness or monotonicity, Distribution testing, Testing properties of bounded degree graphs, Dense graphs and General graphs.

Module: Sketching and Streaming Algorithms

Instructor: Suman Kundu

Extremely Small-Space Data Structures, CountMin Sketch, Count Sketch, Turnstile Streaming, AMS Sketch, Graph Sketching, Graph Connectivity

Module: Map-Reduce

Instructor: Dr. Debasis Das

Map-Reduce Algorithms in Constrains Settings such as small memory, few machines, few rounds, and small total work, Efficient Parallel Algorithms

Module: External memory and Cache-Obliviousness

Instructor: Dr. Debasis Das

Minimizing I/O for large datasets, Algorithms and data structures such as B-trees, Buffer trees, Multiway merge sort

Suman Kundu
Suman Kundu
Assistant Professor of Computer Science and Engineering

My research interests include social network analysis, network data science, streaming algorithms, big data, granular computing, soft computing, fuzzy and rough sets.