# CSL7030: Algorithms for Big Data

• Credits L-T-P [C]: 2-0-0 [2]
• Where: Room-307, Lecture Hall Building
• When: 9:00AM - 9:50AM, Tue-Thu

## Syllabus

• Sketching and Streaming: Extremely small-spaced data structures
• Numerical linear algebra: Algorithms for big matrices, regressing, low-rank approximation, matrix completion
• Compressed Sensing: Sparse signals, linear measurements, signal recovery
• External memory and cache-obliviousness: Minimizing I/O for large datasets, Algorithms and data structures such as B-tree, Buffer tree, Multi-way mergesort

## Learning Materials

1. Scribing of Lectures Algorithms for Big Data By Prof. Jelani Nelson, Harvard University

2. Algorithmic Techniques for Big Data Analysis By Prof. Barna Saha, University of Minnesota Twin Cities

3. Sketching, Streaming and Sub-linear Space Algorithms By Prof. Piotr Indyk, MIT

4. https://www.sketchingbigdata.org/

• Social Engagement: 10% (at Piazza)
• Project: 30% (at Your Place)
• K-DB Creation: 20% (at Your Place)
• Exams (Place to be announced)
• Minor 1: 10%
• Minor 2: 10%
• End-sem Exam: 20%

### K-DB Creation

• Write posts on interesting articles/topics
• Will be added to the course website
• Please follow research ethics (no plagiarism, no stealing, use proper citation and take permission for using diagrams if required)
• Add references/data sets to our course website
• Add or modify wiki to the course website
Please use Markdown syntax for your write ups. A reference guide for Markdown syntax is available at https://www.markdownguide.org/basic-syntax/

### Project

• Survey 50%
Good news: You may work in a group of 2-4 person. Accordingly your group survey needs to be about $5\times group\_members$
Please try to make a group by August $15^{th}$ and let me know.
Please note that the final grade of the course for any B. Tech $4^{th}$ year student opting this course will be as per the earlier rules. That is they will get any one of the following for their performance in the class: A=10, B=8, C=6, D=4, F=0. However, please remember you are also assessed based on the aforementioned Grading Policy.