|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.
|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.