Algorithms For Massive Data

Recent availability of large data sets has had a significant impact on the design of algorithms. While working with big data, classical algorithms are often too inefficient, e.g., they are too slow, or require too much space. This course focuses on algorithms that are specifically designed for large datasets and will cover the following topics.

Some of the new computational models that capture various aspects of massive data computation such as streaming algorithms, and sub-linear time algorithms.
Some of the algorithmic techniques and tools for solving problems over massive data, such as sampling, sketching, dimensionality reduction, and computing efficient summaries of the data (e.g., core-sets).

This is a theoretical course and targets both graduate students and advanced undergraduate students with a strong background in algorithms and discrete mathematics.