Machine Learning for Engineers with MATLAB
CES recently ran this workshop in conjunction with the KAUST Supercomputing Laboratory. Machine Learning (ML)is not a recent concept. Likewise, its adoption by industry is sometimes considered to only be in its early days. In this presentation, we show you how it is already being used in everyday life. Moreover, we gave participants hands-on experience in MATLAB. We also show you what it is, when to use Machine Learning vs Deep Learning and gave hands-on instruction in MATLAB participants were shown how to :
- Access, explore, analyze, and visualize sensor data in MATLAB
- Use the Classification Learner app and functions in the Statistics and Machine Learning Toolbox to perform common ML tasks such as:
- Feature selection and feature transformation
- Specifying cross-validation schemes
- Training a range of classification models, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor, and discriminant analysis
- Performing model assessment and model comparisons using confusion matrices and ROC curves to help choose the best model for your data.
If you are unable see the video above, click here for access to the video recording of the workshop. If you should have any questions about this video or any aspect of Machine Learning, please feel free to contact us
