DATA8008- Scalable Optimization Methods in Data Science
Course Instructor
Dr Yue XIE
Research Assistant Professor
HKU Musketeers Foundation Institute of Data Science and
Department of Mathematics, HKU
Dr. Yue Xie is a Research Assistant Professor in Musketeers Foundation Institute of Data Science (HKU-IDS) and Department of Mathematics at the University of Hong Kong. He was a postdoc at UW Madison working in the nonconvex optimization group led by Professor Stephen J. Wright. He received his PhD degree in Pennsylvania State University and Bachelor degree from Tsinghua University. Dr. Yue Xie has been focusing on algorithm design and analysis to address nonconvex and stochastic optimization problems with all types of applications including machine learning and data science. He has published/served as the referee of top-tier journals including Mathematical Programming, SIAM Journal on Optimization, and IEEE Transactions on Automatic Control, etc. He has delivered numerous presentations at major international conferences such as International Conference on Continuous Optimization (ICCOPT), International Symposium on Mathematical Programming (ISMP), SIAM Conference on Optimization and International Conference on Machine Learning (ICML).
Course Description
This course introduces students to fast and scalable optimization algorithms that play important roles in data science. Students will not only learn about implementation of the algorithms but also understand why they are correct and fast (theoretical guarantees). Both mathematical analysis skills and programming skills of the student will be trained.
Prerequisites
Real Analysis, Linear Algebra, Operational Research, Statics and Probability, Optimization (Linear and Convex). In general, the course will be very much self-contained.
HKU IDS
Research Postgraduate Programme
DATA8008 - Scalable Optimization Methods in Data Science (Computation)
Course Instructor
Dr Yue XIE
Course Description
This course introduces students to fast and scalable optimization algorithms that play important roles in data science. Students will not only learn about implementation of the algorithms but also understand why they are correct and fast (theoretical guarantees). Both mathematical analysis skills and programming skills of the student will be trained.
Prerequisites
Real Analysis, Linear Algebra, Operational Research, Statics and Probability, Optimization (Linear and Convex). In general, the course will be very much self-contained.