AI@Class 11/12:

“This mission  is for the benefit of children with curious minds to whom the future belongs.” It is part of the mission of “ Coming of Age in the 4th Industrial Age”.

The purpose of this program is twofold: one to introduce the young to the emerging field of Artificial Intelligence and Machine Learning and the other to guide them to become more aware self-learners who can continue to learn more about the field. The teaching-learning model is a combination of Flipped Learning and heutagogy with the goal of ‘playful exploration’ of the field. 

We have developed a list of 24 modules, structured as 6 groups/clusters) with 4 modules each; and one week devoted to each module. In all a 6 months program delivered several times a year, with the first cohort  from July 2019 to December 2019 and then a fresh cohort every month from August 2019 onwards.

This is an intensive academic program that a student covers in 24 weeks and  is meant for high ability and highly motivated learners. Not meant for the faint hearted struggling with the regular mandated course load. 

Teaching coding ( in Python) is not included here as there is already a course dedicated to this in the CBSE curriculum. 

Course: Learning to Learn AI

This course consists of 6 clusters , each comprising 4 modules . Each module  is further divided into 5 units, each unit being structured as 5 topics. A nested decimal system is followed in coding the content. 

Cluster 1: Foundations: 

Module 1.1: Why Learn AI

Module 1.2: Smart Learning

Module 1.3: Computational Thinking

Module 1.4: AI and the future of work in the Gig economy

Cluster 2:  The Landscape of AI 

Module 2.1: The Turing test: Meaning of narrow AI and AGI

Module 2.2: Swarm Intelligence

Module 2.3: Rule based expert systems

Module 2.4: Other technologies: IoT, 3D Printing, Blockchain, AR and VR

Cluster 3: Mathematics for AI/ML: 

Module 3.1: Mathematical Modelling

Module 3.2: Linear Algebra

Module 3.3: Calculus for Machine Learning

Module 3.4: Bayesian Machine Learning

Cluster 4: Machine Learning Basics: 

Module 4.1: Learning algorithms

Module 4.2: Decision Tree Learning

Module 4.3: Classification

 Module 4.4: Regression

Cluster 5: Deep Learning: 

Module 5.1: Neural Networks

Module 5.2: Basics of Neuroscience

Module 5.3: Artificial Neural Networks

Module 5.4: A survey of current applications of Deep Learning and future trends

Cluster 6: AI applications: 

Module 6.1: Image and  Object recognition and manipulation

Module 6.2: Speech generation and recognition : chatbots

Module 6.3: Recommendation Engines  and Predictive Models : fake news

Module 6.4: AI and ethics