Please join Texas A&M University High Performance Research Computing and MathWorks on June 7th.
Session 1: Data Analytics and Machine Learning using MATLAB
Using Data Analytics to turn large volumes of complex data into actionable information can help you improve engineering design and decision-making processes. However, developing effective analytics and integrating them into business systems can be challenging. In this seminar, you will learn approaches and techniques available in MATLAB to tackle these challenges. Using machine learning techniques, you will see how you can manage raw data, identify key features that impact your model, train multiple models, and perform model assessments.
Session 2: Optimizing and Accelerating MATLAB Code
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.
Faculty, staff, researchers and students are all welcome to attend.
Saket Kharsikar: Saket joined MathWorks in 2008 as an Application Engineer. He has a master’s degree in biomedical engineering, with a specialization in bioinformatics and computational biology, from the University of Akron. Some of the areas that he focusses on at MathWorks are MATLAB as a technical computing platform, parallel and distributed computing, machine learning and deployment of applications outside of MATLAB
|1:00||Data Analytics and Machine Learning using MATLAB
|2:45||Optimizing and Accelerating MATLAB Code