Importance of Central Limit Theorem in Data Science, Diversity and Inclusion using Reinforcement Learning to Improve Decision Making in Computer Systems.

Date: 27th November 2023
Time: 10.30 am - 3:15 pm
Venue : Symbiosis Institute of Computer Studies and Research
Speaker Of the Event :

  1. Dr. Shilpa Mujumdar, Assistant Professor, SICSR
  2. Dr. Rajashree Jain, Professor, SICSR
  3. Mr. Prathamesh Lahande, Assistant Professor, SICSR

Event details :
No of Participants : 17

The Chishiki sessions of November 2023 have certainly augmented the knowledge of the participants in various areas. The topics for the session were relevant, recent and very much informative. Following is the brief summary of the topics.

The first session was conducted by Dr. Shilpa Mujumdar, who explained how central limit theorem can be utilized in data science. Data Science is a field or domain, which includes and involves working with a huge amount of data from various business domains, using computer science and statistics to build models for prediction and prescribing. Thus, she emphasized that the concept of Central Limit Theorem and normal distribution can be used for prediction in parameter estimation and testing of hypotheses.

Second session, was delivered by Dr. Rajashree Jain, on diversity and inclusion. She said that the Diversity and Inclusion are two interconnected but non-interchangeable terms. Gender Diversity has always been a point of contention. Considering its importance, the UN also has set it as a Sustainable Development Goal (SDG). A number of approaches, solutions and frameworks are available to resolve this still existing wicked problem.

Third session was conducted by Mr. Prathamesh Lahande, who have focused on reinforcement learning technique of Machine Learning. It is about taking suitable action to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path it should take in a specific situation. Sir explained the difference between Reinforcement learning and supervised learning in a way that in supervised learning the training data has the answer key with it so the model is trained with the correct answer itself whereas in reinforcement learning.

Photographs of the event:





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