MATLAB Seminar

MATLAB for Data Analytics

วันจันทร์ที่ 9 กันยายน 2562
ณ ห้องประชุมบานเย็นสายัณหวิกสิต
ตึกมหาวชิรุณหิศ ชั้น 15
08:30 - 12:10

Exclusive Speaker Profile

Dr. Loren Shure

Loren has worked at MathWorks for over 30 years. For the first 27 of these years, Loren co-authored several MathWorks products in addition to adding core functionality to MATLAB, including major contributions to the design of the MATLAB language. She is currently part of the Application Engineering team, enabling Loren to spend more time and energy working with customers.

She graduated from MIT with a B.Sc. in physics and has a Ph.D. in marine geophysics from the University of California, San Diego, Scripps Institution of Oceanography. She is a Senior Member of IEEE.  Loren writes about MATLAB on her blog, The Art of MATLAB

Consulting Application Engineer, MathWorks

Topic Outline

Topic 1: What's New in MATLAB

MATLAB has changed significantly over the last 3 years to address the growing needs of our users, but we've noticed that even experienced MATLAB users aren't taking advantage of our latest and greatest features.

This seminar will cover the newest MATLAB features and show you how to utilize these features to simplify your work, save time, and increase your productivity.


  • MATLAB Foundation Updates

           o   Faster execution engine
           o   Graphics system
           o   Live Editor

  • Data handling and language enhancements

           o   Simpler data importing
           o   Improved datatypes
           o   Big data capabilities

  • App building

           o   App Designer
           o   Sharing apps and creating toolboxes

  • Hardware support

  • Other Toolbox enhancements

Topic 2: Data Analytics with 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 other systems can be challenging.  In this session, you will learn the approaches and techniques available in MATLAB to tackle these challenges.



  • Accessing, exploring, and analyzing data stored in files, databases, and the web

  • Techniques for cleaning, visualizing, and combining complex out-of-memory data sets

  • Prototyping, testing, and refining predictive models using machine learning methods

  • Integrating and running analytics within embedded platforms, enterprise business systems, and interactive web applications


Topic 3: Machine Learning with MATLAB
10.00 - 10.55
Topic 1: MATLAB & Simulink Fundamentals
Topic 2: Image Processing with MATLAB
11.55 -12.00
12.00 -12.30
Topic 4: MATLAB support package for Raspberry Pi or Arduino
12.30 - 13.00
Topic 5: Systems Identification App for Mathematical Models of Dynamic Systems Construction.
9.30 - 10.00