What we're about

This is for people in interested in AI and Machine Learning. All levels of expertise are welcome. The meetup is for anything from getting up to speed with state-of-the-art techniques to contributing to the field.

Update 9/26/19: We now have a Sponsor, Miner and Kasch (https://minerkasch.com/), which is an artificial intelligence and data science consulting firm that builds intelligent end-to-end, data-driven solutions.

A big thank you to M&K, for covering costs of room rental, coffee, meetup.com fees, and incidentals!

Upcoming events (2)

AI Meet & Greet

Online event

This is our regular bi-weekly meeting open to anyone interested in Artificial Intelligence or Machine Learning, regardless of experience or background. The point is to meet others interested in the area and potentially form collaborations or study groups. For those new to AI/ML, please identify yourself so you can connect with others with similar interests. We'll continue the format as the previous meetups - introductions, couple of short presentations, open discussions on topics of interest. A big thank you to our sponsor, Miner and Kasch, for covering the costs of this event! As always, suggestions are most welcome! Please note that this meetup will be recorded. ============= Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance by: Leslie Smith Reaching the performance of fully supervised learning with unlabeled data and only labeling one sample per class might be ideal for deep learning applications. We demonstrate for the first time the potential for building one-shot semi-supervised (BOSS) learning on Cifar-10 and SVHN up to attain test accuracies that are comparable to fully supervised learning. Our method combines class prototype refining, class balancing, and self-training. A good prototype choice is essential and we propose a practical technique for obtaining iconic examples. In addition, we demonstrate that class balancing methods substantially improve accuracy results in semi-supervised learning to levels that allow self-training to reach the level of fully supervised learning performance. Rigorous empirical evaluations provide evidence that labeling large datasets is not necessary for training deep neural networks. Paper: Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance (https://arxiv.org/abs/2006.09363) Code: https://github.com/lnsmith54/BOSS

AI Meet & Greet

Online event

This is our regular bi-weekly meeting open to anyone interested in Artificial Intelligence or Machine Learning, regardless of experience or background. The point is to meet others interested in the area and potentially form collaborations or study groups. For those new to AI/ML, please identify yourself so you can connect with others with similar interests. We'll continue the format as the previous meetups - introductions, couple of short presentations, open discussions on topics of interest. A big thank you to our sponsor, Miner and Kasch, for covering the costs of this event! As always, suggestions are most welcome! ============= Covid-19 severe outcome risk prediction and privacy preserving machine learning. By: Changrong Ji, David Patton and the A3.AI team. A3.AI is a nonprofit Healthcare AI applied R&D organization. 6 senior data scientist volunteers are working on a COVID-19 project, using over 90 million patients' 4 billion health insurance claims, social and mortality data, including over hundreds of thousands of COVID-19 related claims. The goals include predicting a COVID-19 patient's risks of severe outcome, such as hospital admission, ICU admission, ventilator usage using Machine Learning, and doing so in a privacy-preserving manner. In this presentation, the team will share the background of this pro bono effort, data sources, study design and preliminary findings, the basic discoveries as of today.

Past events (32)

AI Meet & Greet

Online event

Photos (50)