Date of Award


Degree Type


Degree Name

Master of Science in Computer Science


Computer Science and Statistics

First Advisor

Marco Alvarez


The field of machine learning (ML) has seen explosive growth over the past decade, largely due to increases in technology and improvements of implementations. As powerful as ML solutions can be, they are still reliant on human input to select the optimal algorithms and parameters. This process is typically done by trial and error, as researchers will select a number of algorithms and choose whichever provides the most desirable result.

This study will use a process called meta-learning to evaluate and analyze datasets and extract a series of meta-features. These features can then be used to intelligently recommend an optimal algorithm, without the cost of having to manually run the algorithm. To accomplish this, we will experiment using 230 datasets and determine their expected outcomes using only the meta-features. The outcomes being optimized are performance (accuracy) and runtime.

Results are ranked in terms of performance and runtime and we can determine how accurately the learning model was able to choose the optimal algorithm for each objective. Additionally, we also run tests to determine the optimal learning rate and weight decay to use when training.



To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.