Building good machine learning models is difficult and time consuming, and few engineers and scientists have the necessary experience. Automated Machine Learning (AutoML) simplifies that process to a few steps, identifying the best model and optimizing its hyperparameters in a single step, thus making machine learning accessible to any engineer.
We will also demonstrate various interpretability methods available in MATLAB that overcome the black box nature of machine learning, lowering the bar to adoption of machine learning in industries that cannot tolerate black box models, including Finance and Medical applications. Finally, we explain how incremental learning makes models improve over time and adopt to changing conditions.
