Deep Conversations on Deep Learning: A technical discussion series IV
Deep Conversations on Deep Learning: A technical discussion series
Interesting conversations of the latest topics in Artificial Intelligence and Machine Learning
Organized by the IEEE Maine Section with presentation resources provided by IEEE Region 1
Organized and hosted by Walter Rawle, Chair, IEEE Maine Section
Occurs the third Wednesday of every month effective 9/16/2020 until 12/16/2020 from 6:00 PM to 7:00 PM, (UTC-04:00) Eastern Time (US & Canada)
Date and Time
Location
Hosts
Registration
- Date: 16 Dec 2020
- Time: 06:00 PM to 07:00 PM
- All times are (GMT-05:00) US/Eastern
- Add Event to Calendar
- Via WebEx
- Augusta/Bangor/Portland, Maine
- United States
- Starts 10 September 2020 09:00 AM
- Ends 16 December 2020 07:00 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Agenda
Deep Conversations on Deep Learning, a technical discussion series, has been organized by the IEEE Maine Section to provide IEEE members technical insight into some of the fascinating emerging developments in the field. Mixing both fundamental concepts and application discussions, the series is intended to spur interest and motivate IEEE members to explore this area further. Presentations have been scheduled for the third Wednesday of each month, September through December, at 6 pm local eastern time, so that members who hold day jobs can participate in the events after work hours. Slide decks will be available on the IEEE Maine Section website after each presentation and the presenters will be available for fifteen minutes after each presentation to answer questions. The IEEE Maine section hopes that this series will be of value to its members and looks forward to suggestions on future technical series presentations. Presentation schedules and topic abstracts are as follows:
When AI Begets AI: the Evolution of Automated Machine Learning and the Future of Human-AI Collaboration: December 16, 6 pm: Presenter Julia Upton
Abstract: In this talk, we will focus on the recent advances in automation of standard Machine Learning practices (such as data preparation, feature engineering, hyper-parameter optimization, and Machine Learning model selection), and the implications for Human-AI collaboration. AI-based automation of the entire pipeline from the raw dataset to deploying and continuously improving Machine Learning models significantly outperforms the “hand” design approach in terms of speed, efficiency, and accuracy. This talk brings a tangible, working level perspective to the conversation around artificial intelligence and machine learning. IEEE Maine hopes that its members find this topic of special interest and that it spurs further curiosity and interest in this fascinating disciple.