Degree Level Course

Algorithms for Data Science (ADS)

The aim of this second-level graduate course is to provide a broad overview and develop the tools and methods necessary for the large-scale problems that naturally arise in many data science-related application areas.

Taught by Arun Rajkumar

Code BSDA5003

Credits 4 Credits

Type Unspecified

Prerequisites

BSCS2007

12-Week Roadmap

Course Structure & Syllabus

For details of standard term assessment timelines and exam structures, visit our Academics page.

WEEK 1

Foundations of Randomized Methods & Concentration Inequalities

WEEK 2

Randomized SVD – I: Basics & Sampling Techniques

WEEK 3

Randomized SVD – II: Applications to PCA & Dimensionality Reduction

WEEK 4

Graph-Based Learning – I: Spectral Graph Theory, Clustering, Community Detection

Reading List

Prescribed Books & References

A. Blum, J. Hopcroft, and R. Kannan (2020) Foundations of Data Sciences, Cambridge University Press
M. W. Mahoney (2010) Randomized Algorithms for Matrix and Data, Foundations and Trends in Machine Learning, pages 123-224

Faculty & Experts

About the Instructors

Arun Rajkumar

Assistant Professor , Department of Data Science and AI , IIT Madras

Faculty Webpage

I am currently an Assistant Professor at the Data Science and AI department of IIT Madras. Prior to joining IIT Madras, I was a research scientist at the Xerox Research Center (now Conduent Labs), Bangalore for three years. I earned my Ph.D from the Indian Institute of Science where I worked on 'Ranking from Pairwise Comparisons'. My research interests are in the areas of Machine learning, statistical learning theory with applications to education and healthcare.

Other Courses by Instructor:

BSCS2004 • Machine Learning Foundations BSCS2007 • Machine Learning Techniques BSDA5007 • Reinforcement Learning

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