DATA8004 - Optimization for Statistical Learning
Course Instructor
Professor Man Chung YUE
Assistant Professor
HKU Musketeers Foundation Institute of Data Science and
Department of Data and Systems Engineering, HKU
Professor Man-Chung Yue is an Assistant Professor jointly affiliated with the Musketeers Foundation Institute of Data Science and the Department of Data and Systems Engineering at The University of Hong Kong. Prior to joining HKU, he was an Assistant Professor in the Department of Applied Mathematics at The Hong Kong Polytechnic University. He worked as a Research Associate at Imperial College London. He received both his Ph.D. in Systems Engineering and Engineering Management and B.Sc. in Mathematics from The Chinese University of Hong Kong.
Course Description
Optimization has long been playing an important, indispensable role in statistics and machine learning. This has become even more so in recent years, since the current era of big data presents many theoretical and computational challenges to the statistics and machine learning communities. This course aims at introducing a series of core topics lying at the intersection of modern optimization and statistics. We will particularly focus on the interplay between sample and computational complexity.
Prerequisites
The prerequisites to this course are undergraduate linear algebra, calculus, real analysis, probability theory and convex optimization.
HKU IDS
Research Postgraduate Programme
DATA8004 - Optimization for Statistical Learning (Computation)
Course Instructor
Prof Man Chung YUE
Course Description
Optimization has long been playing an important, indispensable role in statistics and machine learning. This has become even more so in recent years, since the current era of big data presents many theoretical and computational challenges to the statistics and machine learning communities. This course aims at introducing a series of core topics lying at the intersection of modern optimization and statistics. We will particularly focus on the interplay between sample and computational complexity.
Prerequisites
The prerequisites to this course are undergraduate linear algebra, calculus, real analysis, probability theory and convex optimization.