MATLAB technical sessions at Cornell: October 10-11, 2017
Contact: Paul Redfern
Cell: (607) 227-1865
FOR RELEASE: September 27, 2017
Learn how to accelerate MATLAB algorithms and use MATLAB machine learning algorithms to make critical decisions.
Join MathWorks engineers for free technical sessions on October 10 and 11, 2017 at Phillips Hall and Upson Hall.
This event is brought to you with the support of the Cornell University Center for Advanced Computing (CAC)
who will be leading a hands-on workshop session on parallel computing with MATLAB and scaling to Cornell’s Red Cloud platform.
The agenda is:
|October 10, 2017|
|9:30 am||Registration and sign-in|
|10:00 am –|
|Session 1 (Seminar): Parallel and Distributed Computing with MATLAB |
|An introduction to high-level programming constructs that allow you to parallelize MATLAB applications without CUDA or MPI programming and run them on multiple processors. 219 Phillips Hall.
|1:00 pm –|
|Session 2: (Hands-on Workshop): Parallel Computing with MATLAB and Scaling to Red Cloud|
|Hands-on workshop for more experienced MATLAB users seeking to reduce workflow cycle and solve computationally and data-intensive problems faster by scaling to Cornell’s Red Cloud platform. Led by Cornell CAC Senior Research Associate Steven Lantz. 225 Upson Hall.|
|October 11, 2017|
|10:00 am –|
|Session 1 (Seminar): Machine Learning with MATLAB|
|A primer on machine learning techniques available in MATLAB to quickly explore your data, evaluate machine learning algorithms, compare the results and apply the best technique to your problem. 213 Phillips Hall.|
To view complete event information and to pre-register, visit: www.mathworks.com/cornellmatlab. You may register for individual sessions, or for all three sessions. Walk-ins are welcomed.
Please contact MathWorks account manager Tim Mathieu with questions at 508-647-7016 or firstname.lastname@example.org.
The Cornell University Center for Advanced Computing delivers computing, training, and consulting services to help Cornell faculty, staff,
and student researchers accelerate discovery. Our solutions include HPC hosting, cloud computing, parallel programming, optimization,
scientific workflow design, database design, data analysis, and visualization.
To learn more, visit: https://www.cac.cornell.edu/.