ECE 157A/272A: Machine Learning in Design and Test Automation I

Course Abstract

Applying “machine learning” (ML) for test has been a growing field of interest in recent years. Many potential applications have been demonstrated and tried. In this course, I will start with a review of the basic principles for applying ML in selected test applications and highlights the key challenges.
I will share the experience as how various barriers led us to prioritize those applications and identify the low-hanging fruits in wide variety choices of applications. These include barriers in data, in learning theories, in computational resources, and in competition with existing non-ML based solutions.
At the end of this journey, I will show why “applying ML in practice” in our field can actually mean to build an AI system which in principle is similar to other AI systems such as autonomous vehicles and consequently, the deployment of such an AI system will face a set of test and verification questions regarding the ML components employed in the system.

Course Resources

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Currently, the course materials (lecture slides, videos) are open for access within the UCSB campus only. Selected materials might become publicly assessible after the Fall quarter. Please contact the IEA course website administrator Min Jian for further question.