DATA8014 - Principles of Deep Representation Learning
Course Instructor
Professor Yi MA
Director, HKU Musketeers Foundation Institute of Data Science
Director, HKU School of Computing and Data Science
Professor, Chair of Artificial Intelligence
Professor Yi Ma is a Chair Professor in the Musketeers Foundation Institute of Data Science (HKU IDS) and Department of Computer Science at the University of Hong Kong. He took up the Directorship of HKU IDS on January 12, 2023. He is also a Professor at the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He has published about 60 journal papers, 120 conference papers, and three textbooks in computer vision, generalized principal component analysis, and high-dimensional data analysis.
Professor Ma's research interests cover computer vision, high-dimensional data analysis, and intelligent systems. For full biography of Professor Ma, please refer to: https://datascience.hku.hk/people/yi-ma/
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.
HKU IDS
Research Postgraduate Programme
DATA8014 - Principles of Deep Representation Learning (Foundation)
Professor Yi MA
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.