Explore optimization techniques in this comprehensive masterclass led by Professor Coralia Cartis from the University of Oxford. Delve into algorithm development, analysis, and implementation for various problem classes, including linear, convex, nonconvex, smooth/nonsmooth, and stochastic optimization. Learn about the complexity of nonconvex optimization problems, compressed sensing, and parameter estimation for climate modeling. Discover cutting-edge research on optimization methods for problems with imperfect or corrupted information, such as stochastic optimization. Gain insights from Professor Cartis' extensive experience in numerical optimization and her work at prestigious institutions like the University of Oxford, Rutherford Appleton Laboratory, and the University of Edinburgh. This 1 hour and 53-minute lecture, presented by the Alan Turing Institute, offers a deep dive into advanced optimization techniques suitable for large-scale applications in data science and beyond.
Masterclass for Optimisation - Professor Coralia Cartis, University of Oxford
Alan Turing Institute via YouTube
Overview
Syllabus
Masterclass for optimisation - Professor Coralia Cartis, University of Oxford
Taught by
Alan Turing Institute
Related Courses
-
Optimisation for Machine Learning: Theory and Implementation (Hindi)
-
Optimisation - An Introduction: Professor Coralia Cartis, University of Oxford
-
From Joins to Aggregates and Optimisation Problems - Professor Dan Olteanu, University of Oxford
-
Proximal Gradient Methods for Nonsmooth Nonconvex Minimax - A Unified Convergence Analysis
-
Stochastic Primal Dual Splitting Algorithms for Convex and Nonconvex Composite Optimization in Imaging
