Computational Data Analysis

Prof. C.R. Schwiegelshohn
I semester - 6 credits

The challenges of data analysis have increasingly focussed on scalability issues. Algorithms that used to be regarded as efficient are no longer viable. Since then algorithmic research has begun to address these issues, developing new models of computation and design paradigms.

In this course, we will focus on the design and analysis of a number of these algorithms. Important topics include probabilistic algorithms for data analysis such as clustering and low rank approximation, sublinear-time, and dynamic or on-line algorithms. Aside from the algorithmic techniques, the goal of the course is also to familiarize the students with the mathematical tools necessary to prove performance guarantees.