Algorithms for Data Science, Jan-May 2026

Lecturer: Ravishankar Krishnaswamy

Location: TBD

Course Objectives

The students should be able to:

• Identify if a given algorithmic problem has a structure that makes it solvable.
• Systematically analyse the complexity class of algorithms, and grasp concepts of computational hardness, i.e. NP-completeness etc.
• Identify the conditions for applying various methods for solving the problem.
• Be aware of various algorithmic techniques for modeling and solving problems (randomized algorithms, sketching, streaming, greedy, divide&conquer, dynamic programming, etc.).
• Deconstruct data-science problems into their fundamental computational components and build algorithms for the same.

Tentative Topics

Algorithmic Design Techniques:

• Dynamic Programming
• Greedy Algorithms
• Divide and Conquer

Optimization:

• Gradient Descent and Variants

Algorithms for Big Data:

• Sketching Algorithms
• Streaming Algorithms

Randomized Algorithms:

• Balls and Bins
• Load Balancing

Machine Learning Foundations:

• Clustering Algorithms

High Dimensional Data:

• High Dimensional Geometry
• Locality Sensitive Hashing (LSH)

Announcements

• Course details coming soon.