COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring. |
University of Cambridge > Talks.cam > CCIMI Short Course: Mathematics of Data - From Theory to Computation
CCIMI Short Course: Mathematics of Data - From Theory to Computation
Add to your list(s)
Send you e-mail reminders
Further detail
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. Instructor: Volkan Cevher (Laboratory For Information And Inference Systems, LIONS ) 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. Throughout the course, we put the mathematical concepts into action with large-scale applications from machine learning, signal processing, and statistics. Learning outcomes: By the end of the course, the students are expected to understand the so-called time-data tradeoffs in data analytics. In particular, the students must be able to: Choose an appropriate convex formulation for a data analytics problem at hand Estimate the underlying data size requirements for the correctness of its solution Implement an appropriate convex optimization algorithm based on the available computational platform Decide on a meaningful level of optimization accuracy for stopping the algorithm Characterize the time required for their algorithm to obtain a numerical solution with the chosen accuracy 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; Paula Smith. If you have a question about a specific talk, click on that talk to find its organiser. 0 upcoming talks and 9 talks in the archive. Stochastic gradient & Random coordinate descent methods - Part 3Volkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Thursday 15 March 2018, 16:00-16:45 Stochastic gradient & Random coordinate descent methods - Part 2Volkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Thursday 15 March 2018, 15:00-15:45 Stochastic gradient & Random coordinate descent methods - Part 1Volkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Thursday 15 March 2018, 14:00-14:45 Constrained convex minimization - Part 3Volkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Wednesday 14 March 2018, 16:00-16:45 Constrained convex minimization - Part 2Volkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Wednesday 14 March 2018, 15:00-15:45 Constrained convex minimization - Part 1Volkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Wednesday 14 March 2018, 14:00-14:45 Variable metric methodsVolkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Tuesday 13 March 2018, 16:00-16:45 Unconstrained smooth minimizationVolkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Tuesday 13 March 2018, 15:00-15:45 Introduction to convex optimization and iterative methodsVolkan Cevher (Laboratory For Information And Inference Systems, LIONS). MR9 Centre for Mathematical Sciences. Tuesday 13 March 2018, 14:00-14:45 Please see above for contact details for this list. |
Other listsMRC Human Nutrition Research C2AD seminar Series Art Cell GalleryOther talksThe semantics and pragmatics of racial and ethnic language: Towards a comprehensive radical contextualist account Chemical convection and stratification at the top of the Earth's outer core Primary liver tumor organoids: a new pre-clinical model for drug sensitivity analysis Uncertainty Quantification with Multi-Level and Multi-Index methods Don't be Leeroy Jenkins – or how to manage your research data without getting your whole project wiped out |