Upcoming Seminar

Free Technical MATLAB Seminars at George Washington University

George Washington University
Room B1220 (in Science and Engineering Hall)
September 13, 2017
Session 1: 1:00 – 3:00 p.m.
Session 2: 3:30 – 4:30 p.m.


Time Title
1:00 – 3:00 p.m. Machine Learning with MATLAB Seminar

Engineers and data scientists work with large amounts of data in a variety of formats such as sensor, image, video, telemetry, databases, and more. They use machine learning to find patterns in data and to build models that predict future outcomes based on historical data.

In this session, we explore the fundamentals of machine learning using MATLAB. We introduce 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.

Highlights Include:

  • Training, evaluating and comparing a range of machine learning models
  • Using refinement and reduction techniques to create models that best capture the predictive power of your data
  • Running predictive models in parallel using multiple processors to expedite your results
  • Deploying your models in a variety of formats
3:30 – 4:30 p.m. Parallel and Distributed Computing with MATLAB

Large-scale simulations and data processing tasks support engineering and scientific activities such as mathematical modeling, algorithm development, and testing can take an unreasonably long time to complete or require a lot of computer memory. You can speed up these tasks by taking advantage of high-performance computing resources, such as multicore computers, GPUs, computer clusters, and cloud computing services.

Using the Parallel Computing capabilities in MATLAB allows you to take advantage of additional hardware resources that may be available either locally on your desktop or on clusters and clouds. By using more hardware, you can reduce the cycle time for your workflow and solve computationally and data-intensive problems faster.

We will discuss and demonstrate how to perform parallel and distributed computing in MATLAB. We will introduce you to parallel processing constructs such as parallel for-loops, distributed arrays, and message-passing functions. We will also show you how to take advantage of common trends in computer hardware, from multiprocessor machines to computer clusters.

Highlights Include:

  • Built-in support for parallel computing
  • Creating parallel applications to speed up independent tasks
  • Scaling up to computer clusters, grid environments or clouds
  • Employing GPUs to speed up your computations
  • Programming with tall and distributed arrays to work with large data sets