Course Description 

This course aims to provide a rigorous and systematic introduction to the mathematical and computational principles of deep learning. We achieve this by centering the course around a common and fundamental problem behind almost all modern practices of artificial intelligence and machine learning such as image recognition and generation. The problem is how to effectively and efficiently learn a low-dimensional distribution of data in a high-dimensional space and then transform the distribution to a compact and structure representation. Such a representation can be generally referred to as a memory learned from the sensed data. 

Prerequisites 

Some background in undergraduate linear algebra, statistics, and probability is required. Background in signal processing, information theory, optimization, feedback control may allow you to appreciate better certain aspects of the course material, but not necessary all at once.