The 'academic' component of the EE Program covers multiple pillars of the GenWise Curriculum. These curricular elements will be covered over 2 weeks (4-5 hrs/ day), as follows:
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Week 1: Mathematics, Science, Design and Technology (STEM Focus)
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Week 2: Nature, Society &Individual; Tools for Thinking & Communication (Humanities Focus)
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Please click on each week to know (MUCH) more...
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While children are free to pick either week, we strongly recommend a balanced exposure to the content over both weeks.


Navin Kabra
Navin Kabra is CTO and Co-Founder at ReliScore, a company that provides skill and capability assessment solutions to the software industry. He also consults and advises multiple GoI initiatives, as well as fintech companies in the private sector (Innoviti - payments processing, and FinIQ - derivatives and other financial products).
Navin has several peer-reviewed articles in international conferences / journals and is also an inventor on 18 US Patents, 2 European Patents, and 1 Japanese Patent, filed as part of his work for 3 different companies (Symantec, Veritas, TeraData).
Navin has an undergrad degree (IIT Bombay) and a Ph.D (Univ of Wisconsin, Madison), both in Computer Science.
Artificial Intelligence & Machine Learning: An Introduction
By far, the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.
Eliezer Yudkowsky, Co-Founder, Machine Intelligence Research Institute
Date(s):
May 7-28, 2023

Gifted Summer Program May 2022

Gifted Summer Program May 2022

Gifted Summer Program May 2022

Gifted Summer Program May 2022
Artificial Intelligence (AI) and Machine Learning (ML) are changing the world, and there is going to be an increasing demand for the people who can understand and build AI/ML systems.
All aspects of our life are being digitized and data is being captured, and much of this data is available to anybody who wants to do something with it. More and more of our day-to-day activities will be controlled by or influenced by algorithms. Which means that the most exciting technologies today (and the ones that are also the most successful) are the ones that can make sense of this data in a way humans can. Every company, from Google to Facebook to Netflix to Amazon to Flipkart are turning to AI/ML for best results.
This course will give students a hands-on introduction to Artificial Intelligence and Machine learning. The course will introduce the students to 3 different aspects of AI theory and
practice:
Understanding the theory of AI/ML
Learning how to build simple machine learning systems using low-code/no-code tools
Using AI algorithms with some existing data-sets to solve problems and build games
Specifically, the course will cover the following:
High-Level Concepts
What are Artificial Intelligence and Machine Learning?
How is it different from normal software and computers?
An overview of the most important different approaches to AI
What types of problems they're best suited for?
And, what are some current applications that they're being used for?
The weaknesses of AI
The problems caused by inappropriate use of AI
Theory
Introduction to the mathematics that underlies ML algorithms
Understanding the train-test-validation cycle
Understanding the concept of hyperparameters and tuning
Understanding overfitting, underfitting, and the bias vs. variance trade-off
Techniques for improving accuracy
Building
Image/audio/video/text classification using ML
Build simple games using ML and Scratch
Analyzing some real-world datasets using Python
Final Course Project