ECE 287B: Universal Information Processing

Course description

How can data be compressed with the minimum number of bits? How can an asset be invested in the stock market with the maximum growth of wealth? Or how can a future signal be predicted from the past with the least amount of distortion? Over the past four decades, several algorithms have been developed for such tasks that achieve essentially optimal performance with no prior knowledge about the statistical properties of the data.

This course studies theoretical foundations on such universal algorithms for probabilistic (random data) and deterministic (nonrandom data) settings, building on the notion of universal probability. The main focus is on applications, including data compression, prediction, portfolio selection, entropy estimation, and classification. Other applications will be also explored through final projects.

Prerequisite

Working knowledge of probability.

Course requirements and grading

Lectures

Teaching staff