This short course is organised by the CCIMI , and open to all. Lectures run 2-5pm, with two 15 minute breaks, Tuesday 13th March – Thursday 16th.
Description: Convex optimization offers a unified framework in obtaining numerical solutions to data analytics problems with provable statistical guarantees of correctness at well-understood computational costs.
To this end, this course reviews recent advances in convex optimization and statistical analysis in the wake of Big Data. We provide an overview of the emerging convex data models and their statistical guarantees, describe scalable numerical solution techniques such as stochastic, first-order and primal-dual methods. Throughout the course, we put the mathematical concepts into action with large-scale applications from machine learning, signal processing, and statistics.
Prerequisites: Previous coursework in calculus, linear algebra, and probability is required. Familiarity with optimization is useful.
It is useful for students to have a laptop/tablet (or even a smartphone) for some of the more practical examples, but this is not necessary. Those without computer access can follow a demo shown by the instructor.
If you have a question about this list, please contact: Rachel Furner; J.W.Stevens. If you have a question about a specific talk, click on that talk to find its organiser.