Upcoming Seminar

Data Analytics and Machine Learning using MATLAB & Optimizing and Accelerating MATLAB Code

Location:
Texas A&M University
Venue:
Interdisciplinary Life Sciences Building (ILSB)
Room 1105 (Auditorium)
Date:
June 7, 2017
Time:
Session 1: 1:00 – 2:30 p.m.
Session 2: 2:45 – 4:15 p.m.

Overview

Please join Texas A&M University High Performance Research Computing and MathWorks on June 7th.

Highlights

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.

Highlights include:

  • Accessing, exploring, and analyzing data stored in files, the web, and data warehouses
  • Techniques for cleaning, exploring, visualizing, and combining complex multivariate data sets
  • Prototyping, testing, and refining predictive models using machine learning methods
  • Integrating and running analytics within enterprise business systems and interactive web applications

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.

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

Who Should Attend

Faculty, staff, researchers and students are all welcome to attend.

About the Presenter

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

Agenda

Time Title
1:00 Data Analytics and Machine Learning using MATLAB
  • Accessing, exploring, and analyzing data stored in files, the web, and data warehouses
  • Techniques for cleaning, exploring, visualizing, and combining complex multivariate data sets
  • Prototyping, testing, and refining predictive models using machine learning methods
  • Integrating and running analytics within enterprise business systems and interactive web applications
2:30 Break
2:45 Optimizing and Accelerating MATLAB Code
  • 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
4:15 Q&A