Ph.D. Dissertation Defense - Jason Zutty

Event Details

Monday, October 29, 2018

3:00pm - 5:00pm

Location: 
Room 5126, Centergy

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Event Details

TitleAutomated Machine Learning: A Biologically Inspired Approach

Committee:

Dr. Aaron Lanterman, ECE, Chair , Advisor

Dr. Justin Romberg, ECE

Dr. Jennifer Michaels, ECE

Dr. Mark Davenport, ECE

Dr. May Wang, BME

Dr. Greg Rohling, GTRI, Co-Advisor

Abstract:

Machine learning is a robust process by which a computer can discover characteristics of underlying data that enable it to create a model for making future predictions or classifications from new data. Designing machine learning pipelines, unfortunately, is often as much an art as it is a science, requiring pairing of feature construction, feature selection, and learning methods, all with their own sets of parameters. No general machine learning pipeline solution exists; each dataset has unique characteristics that make a particular set of methods and parameters better suited to solving the problem than others. To respond to the challenge of machine learning pipeline design, the field of automated machine learning (autoML) has recently emerged. AutoML seeks to automate the often arduous work of a data scientist, so they can focus on the underlying meanings of the data and spend less time on the tedium of pipeline design and tuning. This dissertation adapts and applies genetic programming to the newly emergent field of automated machine learning. Genetic programming enables the artificial evolution of an algorithm through a nearly infinite search space that otherwise requires a randomized search. This dissertation shows that through the process of genetic programming, it is possible to produce machine learning pipelines, and the evolved pipelines can outperform those created by human researchers.

Last revised October 19, 2018