Machine Learning: Fundamentals and Examples

Machine learning algorithms are ubiquitous today. They are in our pockets, our homes and workplaces, our trains, planes and automobiles. Algorithms are beating champion game players, making scientific discoveries, reading, writing and creating artwork without explicit rules or instructions on how to do so. Some argue that general artificial intelligence is on the horizon and that machines might out-think humans soon.

In this session we won’t get to the bottom of the nature of intelligence, but we will scratch the surface. After a brief history of research in artificial intelligence, we will cover basic principles of mathematical modeling and introduce modern machine learning methods. We will discuss how human brains have inspired today’s best-performing and most widespread algorithms called artificial neural networks and explore examples of their usage in real-world scientific and engineering problems. We will cover the practicalities of supervised machine learning including data collection, model selection, training pipeline and evaluation.

The workshop is presented by Dr. John Woodruff and hosted by the Traverse Area District Library as part of the tccodes talent development initiative. It is intended for professional software developers or those with significant coding experience.

For additional information, please visit tccodes.org or email [email protected]

About Dr. John Woodruff

Dr. John Woodruff is an audio and machine learning algorithm developer at Apple, where he has been the technical lead for iOS and AirPods Pro features designed to improve sound for users with hearing difficulty. His work at the intersection of audio technology, hearing science and machine learning has helped many people hear better and increased awareness around hearing difficulties and sound exposure. John has been at the forefront of deploying machine learning in embedded systems for audio in consumer electronics, at Apple and prior to that with Knowles Electronics and Audience, Inc. John received his Ph.D. in computer science and engineering with a focus in artificial intelligence from the Ohio State University in 2012 where he specialized in binaural hearing and modeling how brains organize sound. He received a Master of Music degree from Northwestern University in 2006 working jointly in the School of Music and Computer Science Department, and a B.S. in mathematics from the University of Michigan in 2004.

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