MATLAB Seminars at University of Nevada, Reno

Location:  University of Nevada, Reno – Joe Crowley Student Union:  CSU Room #324

 Date:  September 21, 2017

 Session 1:  9:30 – 10:45 a.m.

Session 2:  10:45 a.m. – 12:00 p.m.

To learn more or to register, please visit >>Register

 Overview

Part 1: Data Analysis and Visualization using MATLAB (9:30 – 10:45 a.m.)

This session is intended for beginning users and for existing users to see how MATLAB has changed over the past few years with some of the latest features. No prior programming experience or knowledge of MATLAB is assumed. MATLAB is a programming environment for algorithm development, data analysis, visualization, and numerical computation. Using MATLAB, you can solve technical computing problems faster and easier than with traditional programming languages. We will provide an overview of MATLAB and introduce you to the powerful statistical analysis and visualization capabilities available as well as demonstrate how to analyze and visualize data, introduce desktop tools for editing code, and show you how to publish and share your results.

Highlights include:

  • Accessing data from files, spreadsheets and other sources
  • Performing statistical analysis, curve and surface fitting routines
  • Developing algorithms and applications to automate your workflow
  • Generating reports in HTML and other file formats to share your work
  • Developing and sharing MATLAB apps, standalone executables and software components

 

Part 2: Optimizing and Accelerating MATLAB Code (10:45 a.m. – 12:00 p.m.)

In this session, we will discuss and demonstrate simple ways to improve and optimize your code that can boost execution speed by orders of magnitude. We will also address common pitfalls in writing MATLAB code, explore the use of the MATLAB Profiler to find bottlenecks, introduce our parallel computing tools to solve computationally and data-intensive problems on multicore computers and clusters, and finally talk about tools to automatically translate your MATLAB code into C.

Highlights include:

  • Optimizing MATLAB code to boost execution speed
  • Automatically generating portable C code from MATLAB
  • Employing multi-core processors and GPUs to speed up your computations
  • Scaling up to computer clusters, grid environments or clouds