CSL7030: Algorithms for Big Data (fall 2019)

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/

Grading Policy

• 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%

Piazza Link for the class:

• Classroom URL: https://piazza.com/iitj.ac.in/fall2019/csl7030/home
• Joining URL: https://piazza.com/iitj.ac.in/fall2019/csl7030
• Access Code: Please ask me at the classroom!

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

Project

• Survey 50%
  • Read about 5 papers
    • about 40% for background study and 60% for recent developments
  • Out of the class presentation/discussion
• Presentation + Write-up 50%
  • Goal to have a project worthy of publication in good conference