Hello techies,

We are thrilled to announce our next session is in collaboration with Assam Artificial Intelligence Initiative and on this session we talk on Reinforcement Learning (RL) and the Know-how of pursuing MS in the USA, to be organized on 20 February 2021 at 10 AM (IST) by Ankur Deka, MS in Robotics Institute, CMU, USA (ongoing)!

RSVP now and submit your question for the speaker:

https://forms.gle/tk775p9TajTcoL5K6

About speaker:

Ankur Deka is currently pursuing Master of Science in Robotics Institute, Carnegie Mellon University, USA. He did his B.Tech in ECE from the Indian Institute of Technology Guwahati (IITG) with CSE minor. He is interested in developing algorithms for deploying real-world robots that can evolve by learning and work seamlessly in uncertain environments.

Linkedin: https://www.linkedin.com/in/ankur-deka/

Google scholar: https://scholar.google.com/citations?user=CaBIO8cAAAAJ&hl=en

GitHub: https://github.com/Ankur-Deka

Abstract:

Several real-world problems require an agent (could be a human or a robot) to take sequential actions - playing games, walking, cooking, the list goes on. It is hard to hand-code these actions in many scenarios. RL is a branch of Machine Learning that enables an agent to excel at a sequential action task through practice, trial and error, and a reward/punishment signal. Imagine how you learned skills in the real world through trial and error and practice. In this session, I will introduce you to RL and Multi-Agent RL. I will also talk about how you can prepare yourself for MS in the USA.

ATLS – 5 to be live-streamed on the following YouTube link –

https://youtu.be/L7cZIwLQZoE

You can also join the event on Google meet using the following link –

https://meet.google.com/qdx-byiv-cjq

Stay tuned for more updates!

We are looking forward to meeting you at the online event.

Please feel free to pass on this message to anyone who might be interested.

#aaiiaxom #AI #machinelearning #datascience #artificialintelligence #deeplearning #NeuralNetworks #robot #reinforcementlearning #cv