AI for Everyone

Welcome to AI for Everyone

Have you ever wondered what is AI and how it is going to affect your life, career and business? Are you a Product Manager, a Designer, or someone who doesn't necessarily write code for a living but are nevertheless fascinated by Technology and how it profoundly affects the human experience?
We are teaching a free in-person AI course to demystify AI for people without (or less) technical background. The class has two parts, in the first part (4 sessions), we explore areas such as supervised/unsupervised learning, neural network and deep learning without any coding requirements. The second part of the class (3 sessions) is more technical and we teach how to use tools like Tensorflow to build AI models. The second part is optional and requires a minimum knowledge of python.
Time: Mondays 7 pm to 8:30 pm. From May, 07, 2018 to June, 25, 2018
Location: eBay office, 625 6th Ave, New York, NY 10011

We are accepting a limited number of students. If you are interested please apply at Here
Please note the class is in-person and it is not offered online. Application deadline: May, 01, 2018

Instructors


Giri Iyengar, PhD
MIT, Computer Science
 Giri heads Computer Vision for eBay. He has been doing Machine Learning and AI since early 2000 at IBM Research where he started his career as a Research Scientist working on teaching Computers to read lips (think HAL 9000 from the Space Odyssey). In addition to building Computer Vision products for eBay, Giri also teaches a Data Science course at Cornell in their NYC Tech campus.

Davood Shamsi, PhD
Stanford University, Management Science and Engineering
 Davood is the founder of Avery.ai, a virtual data scientist for marketers. Davood leads various AI research and engineering projects at Avery.ai covering Natural Language Generation, Graph Optimization, and Machine Learning. Previously, he was developing machine learning and optimization algorithms for large scale systems at Advertising.com/AOL/Oath.

Topics

Supervised learning
Unsupervised learning
Reinforcement learning
Neural network and deep learning
3 optional coding/deep dive sessions