MathWorks Workshop

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


• 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 to production in a variety of formats

Biography: Gen Sasaki is a Principal Customer Success Engineer at the MathWorks, working to ensure university educators and students get the most out of MATLAB.  He holds a BSME and MSME with a focus on control systems.  He in automotive and aerospace applications for nearly 30 years, in powertrain, various embedded controls, and functional safety.