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:
Week 1: Mathematics, Science, Design and Technology (STEM Focus)
Week 2: Nature, Society &Individual; Tools for Thinking & Communication (Humanities Focus)
Please click on each week to know (MUCH) more...
While children are free to pick either week, we strongly recommend a balanced exposure to the content over both weeks.
Artificial Intelligence & Machine Learning: An Introduction
For Ei ATS Scholars (and equivalent): AI and ML are changing our world rapidly. Understand and learn the skills to build AI/ ML systems. Project Based Learning approach - including analyzing real-world datasets using Python, image/audio/video/text classification using AI/ ML, building games, etc.
For Ei ATS Scholars (and equivalent);
Students in Grades 8/9/10 in 2022-23
Artificial Intelligence (AI) and Machine Learning (ML) are changing the world, and there is going to be an increasing demand for people who can understand and build AI/ML systems. More and more of our day-to-day activities will be controlled by or influenced by algorithms. 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. 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. We 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 the Python programming language
- Using AI algorithms with some existing data-sets to solve problems and build games
High level concepts such as how AI/ ML are different from normal software and computers, the most important different approaches to AI and what types of problems they are best suited for, will be covered. Students will also learn important theories such as the mathematics that underlies ML algorithms, overfitting, underfitting, and the bias vs variance trade-off. Programming will cover an introduction to basics of Python, ML programming in Python, image classification, simple neural networks and Natural Language Processing (NLP).
Using this basic understanding and skill set, students will work on some projects like- analyzing real-world datasets using Python, image/audio/video/text classification using ML and building simple games using ML and Scratch.