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BSDA5003 - Algorithms for Data Science (ADS)
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BSDA5003 - Algorithms for Data Science (ADS)
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| Field | Value |
|---|---|
| Course Code | BSDA5003 |
| Level | Degree Level Course |
| Credits | 4 |
| Type | Pre-requisites: |
| Pre-requisites | BSCS2007 - Machine Learning Techniques |
📖 Description
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.
🗓️ Weekly Syllabus
| Week | Topic |
|---|---|
| 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 |
📚 Books & Resources
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
Arun Rajkumar
Assistant Professor,
Department of Data Science and AI,
IIT Madras
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.
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Other courses by the same instructor:
BSCS2004 -
Machine Learning Foundations
,
BSCS2007 -
Machine Learning Techniques
and
BSDA5007 -
Special topics in Machine Learning (Reinforcement Learning)