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Unit 1: Introduction to Artificial Intelligence (AI)

SUB-UNIT LEARNING OUTCOMES SESSION/ACTIVITY/PRACTICAL

AI Project Understand the stages of the AI Session: Revisiting AI Project Cycle
Cycle Project Cycle.

Understand the concept of Artificial
Introduction Intelligence (AI) domains and the
to AI Session: The three domains of AI and their applications.
Domains illustrations of practical applications
within each AI domain.

Session: Frameworks, Ethical Framework and need of Ethical
Frameworks for AI.
Learn about the ethical framework
Ethical for AI and its category. Explore Activity: My Goodness https://www.my-goodness.net/
Frameworks
of AI Bioethics, a popular framework that Session: Types of Ethical Frameworks.
is used in the healthcare industry.
Session: Bioethics and a case study in bioethics.

Unit 2 : Advance Concepts of Modeling in AI

SUB-UNIT LEARNING OUTCOMES SESSION/ACTIVITY/PRACTICAL

Revisiting AI, ML, Understand AI, ML and DL Session: Differentiate between AI, ML, and DL
DL Session: Common terminologies used with data
Session: Types of AI Models: Rule Based Approach, Learning
Based Approach
Session: Categories of Machine learning based models:
Supervised Learning (https://teachablemachine.withgoogle.
com/), Unsupervised Learning
● Familiarize with supervised,
unsupervised and reinforcement (https://experiments.withgoogle.com/ai/drum- machine/view/),
learning based approach Reinforcement Learning
Modeling
● Understand subcategories of Session: Subcategories of Supervised Learning Model:
Supervised, Unsupervised and Classification Model, Regression Model
deep learning models
Session: Subcategories of Unsupervised Learning Model:
Clustering, Association

Session: Subcategories of Deep Learning:
Artificial Neural networks (ANN), Convolutional Neural Network
(CNN)
Session: What is Neural Network?

● Understand Neural Networks Session: How does AI make a Decision?
Artificial Neural
Networks ● Understand how AI makes a Activity: Human Neural Network – The Game
decision
Suggested Neural Network Activity:
https://playground.tensorflow.org/