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.
Course ID: BSDA5003
Course Credits: 4
Course Type:
Pre-requisites: BSCS2007 - Machine Learning Techniques
Course structure & Assessments
For details of standard course structure and assessments, visit 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 |
| WEEK 5 | Graph-Based Learning – II: Graph-Based Ranking |
| WEEK 6 | Dimension Reduction with Johnson-Lindenstrauss Lemma |
| WEEK 7 | Approximate Nearest Neighbors (ANN) – I: LSH & Similarity Search |
| WEEK 8 | Approximate Nearest Neighbors (ANN) – II: MinHash, SimHash, Bloom Filters |
| WEEK 9 | Randomized Methods for Regression |
| WEEK 10 | Matrix Sketching for Machine Learning |
| WEEK 11 | Streaming Algorithms – I: Count-Min Sketch, Heavy Hitters, Frequency Moments |
| WEEK 12 | Streaming Algorithms – II: Reservoir Sampling, Graph Streams, Streaming PCA |
Prescribed Books
The following are the suggested books for the course:
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
About the Instructors
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 the same instructor: BSCS2004 - Machine Learning Foundations , BSCS2007 - Machine Learning Techniques and BSDA5007 - Sequential Decision Making
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