Mar 11 (Fri) @ 11:30am: "Domain Specific Machine Learning – A No-Free-Lunch Perspective,” Matthew Nero, ECE PhD Defense
This work explores Domain Specific Machine Learning (DSML) problems that arise from a workflow in a semiconductor company. The focus is on understanding what characteristics make them different from a typical machine learning problem. We first discuss a general setup for a DSML problem and provide two postulates to describe the scope of the DSML considered in this research. We examine the theories of machine learning, including four different schools of thinking for articulation of what machine learning is. We review the No Free Lunch Theory for machine learning and discuss the incompleteness of the four theories in view of the No Free Lunch.
Based on the theoretical understanding, we then point out the essence of DSML. Specifically, a DSML problem considered in this work follows an iterative process. In each iteration, the learning goal is to attain a "surprise" for the next iteration. Essentially, the unpredictability in the data appears in the next iteration represents the value of the learning. From this point of view, we argue that the underlying problem in DSML is local No Free Lunch.
An important consequence of this view is that machine learning will no longer be used in making a decision for the user, as that traditionally used. Instead, it is used to help a data summarization process to facilitate user to make a decision. In this thesis, we investigate two types of applications, outlier screening in production testing and wafer map pattern recognition for yield monitoring and improvement. We use these two applications to illustrate our view of DSML as well as various considerations associated with the view. Based on experience of implementing a machine learning software solution in a company, we explain the lessons learned for deploying a successful machine learning solution. If we consider traditional machine learning as largely focusing on the learning algorithms, we conclude that DSML is largely focusing on all other aspects trying to leverage the capability of a learning algorithm (if possible). From this angle, DSML is solving a problem complementary to machine learning (Co-ML). In other words, DSML shall not be taken as another direct extension of ML. Instead, it would be more appropriate to view DSML (in the semiconductor industry) as Co-ML.
Matthew Nero is a graduate student in the IEA lab at UCSB, he received his MS and BS in Computer Engineering at UCSB. Matthew has interned for NXP and GLOBALFOUNDRIES, prior to attending UCSB he served as a military intelligence linguist for 6 years. Matthew's area of research is in applying ML within the silicon manufacturing industry.
Hosted by: Li-C. Wang
Submitted by: Matthew Nero <firstname.lastname@example.org>