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ML Discussion Groups MEGA MEETUP
May 25, 2021 @ 6:00 pm - 7:00 pm EDT
Please register in Eventbrite link
Join our technical discussion group showcase where group leads will give brief overviews of the topics they discussed and the best recipes they created over the past 5 weeks & we will answer any questions for the next batch. Discussion groups run every 5 weeks on an array of topics for junior & senior ML practitioners as well as some other emerging tech topics, but all levels are welcome.
LEARN MORE DETAILS AND SIGN UP FOR UPCOMING GROUP HERE
Event Details (times are in ET):
6:00 PM – Welcome & Introduction
6:10 PM – AI Discussion Group Overview
6:15 PM – Discussion Group Showcase (five 5-10 min presentations)
7:00 PM – Networking Breakout Session / Q&A
7:10 PM – Discussion Group Showcase (five 5-10 min presentations)
8:00 PM – Event Ends
AISC is bringing backing our ‘socratic circles’ format in the form of AI DISCUSSION GROUPS! The old format produced rigorous discussions and debates on recent impactful papers in ML, a healthy environment for junior and senior ML folks to teach and be taught, catalyzed long-term friendships, and overall developed a sense of community in the AI/ML community space.
Now we’re back, but at a larger scale, both in the breadth of topics covered and in terms of location-independence. We are starting 10 (and more to come) discussion groups on various topics on ML, led by experts in the field. The discussion groups will focus not only on learning but also on creation – sharing what we have learned to the world in the form of learning recipes featuring original content like community blog posts, code, and videos. Every once in a while, the discussion groups will contain cameo appearances by leading researchers in the field!
Each discussion group will have its own format, but it usually consists of a weekly touchpoint. Below is a list of discussion groups and their respective groups leads.
LEARN MORE DETAILS AND SIGN UP HERE
Recent Trends in Natural Language Processing – Suhas Pai & Zach Nguyen
MLOps and Engineering – Denys Linkov & Ali Darbehani
Reinforcement Learning – Abdul Rahman Sattar & Manjeet Kaur
Building your AI Product Stack – Ashley Beattie
Machine Learning Ethics – Willie Costello & Somaieh Nikpoor
Machine Learning in Healthcare – Karthik Bhaskar & Mary Fallah
Machine Learning in Finance – Serena McDonnell & Sina Dibaji
Time Series Forecasting – Ozan Ozyegen
Quantum Computing – Kris Kaczmarek & Josiah Sinclair
Cybersecurity Analytics – Abdul Rahman Sattar & Apurva Kumar
Machine Learning in Supply Chain – Om Patri & Candice Cloutier
Blockchain and Web 3.0 – Stuart Culpepper & Noah Workman
Graph Neural Networks – Nabila Abraham & Karim Khayrat
Machine Learning in Physics – Sajeda Mokbel & Arash Feizpour
Environmental Data Science – Andre Erler & Yan Nusinovich
Machine Learning in Bioinformatics – Kevin McPherson & Willy Rempel
TinyML (Machine Learning on Edge) – Rouzbeh Afrasaibi & Abdul Rahman Sattar