UCF Research Cyberinfrastructure (RCI)

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 and deep 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 and deep learning using MATLAB. We introduce machine learning techniques available in MATLAB to quickly explore your data, evaluate algorithms, compare the results and apply the best technique to your problem.

Engineers who rely only on Python may find themselves encountering difficult or challenging tasks when it comes to embedded applications, building interactive dashboards, parallelizing applications, and deep learning. Contrarily, MATLAB is a full-stack advanced analytics platform that empowers domain experts to rapidly prototype ideas, validate models, and push applications into production with ease. However, sometimes it is advantageous to integrate MATLAB and Python together. One example being the need to combine MATLAB's vast library of advanced analytics capabilities with supplemental models available in the open-source community. Another, using Python as a language that is well suited to pipe data between different IT systems or the web.

There are several ways to integrate MATLAB and Python together either as R&D tools or as scalable components of your production infrastructure. The latter giving business users and decision makers immediate access to many of MATLAB's built-in analytics capabilities from deep learning, optimization, signal and image processing, computer vision, data mining, time-series forecasting, embedded code generation, and more.

In this session we demonstrate the many ways in which MATLAB and Python can interface and integrate with each other.