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:

• 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

MATLAB 2021 is available on nanoHUB as the simulation tool MATLAB R2021a and includes most toolboxes, Simulink®, and Simulink-based products.

Gaby Arellano-Bello holds a BSME and MSBSE. During her academic and professional career, she focused on data analysis projects and creating apps using MATLAB. She is currently a Customer Success Engineer at MathWorks, supporting academics with their teaching and research activities at US and Latin America universities.

Network for Computational Nanotechnology (NCN)

Researchers should cite this work as follows:

• Gaby Arellano Bello (2022), "Machine Learning with MATLAB," https://nanohub.org/resources/35954.

  BibTex | EndNote

1. algorithms
2. data science
3. machine learning
4. MATLAB
5. MATLAB Tool
6. NCN Group - Machine Learning Hands-on