Artificial Intelligence (Class IX)

Artificial Intelligence (Class IX)

Rs.2,000.00

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18% GST Extra

SKU: cid_188509 Category:
About the Program

Learn Artificial Intelligence from the basics

Duration
  • 1 Month
Delivery Mode
  • Online interactive session on zoom
Test & Evaluation

1. During the program, the participants will have to take all the assignments given to them for better learning.

2. At the end of the program, a final assessment will be conducted.

Certification

1. All successful participants will be provided with a certificate of completion.

2. Students who do not complete the course / leave it midway will not be awarded any certificate.

Course Content

Unit 1: INTRODUCTION TO AI

Excite

Session: Introduction to AI and setting up the context of the curriculum

Ice Breaker Activity: Dream Smart Home idea

Learners to design a rough layout of the floor plan of their dream smart home.

Recommended Activity: The AI Game

Learners to participate in three games based on different AI domains.

  • Game 1: Rock, Paper, and Scissors (based on data)
  • Game 2: Mystery Animal (based on Natural Language Processing - NLP)
  • Game 3: Emoji Scavenger Hunt (based on Computer Vision - CV)

Recommended Activity: AI Quiz (Paper Pen/Online Quiz)

Recommended Activity: To write a letter

Writing a Letter to one’s future self

  • Learners to write a letter to self-keeping the future in context. They will describe what they have learned so far or what they would like to learn someday

Relate

Video Session: To watch a video

Introducing the concept of Smart Cities, Smart Schools, and Smart Homes

Recommended Activity: Write an Interactive Story

Learners to draw a floor plan of a Home/School/City and write an interactive story around it using Story Speaker extension in Google docs.

Purpose

Session: Introduction to sustainable development goals

Recommended Activity: Go Goals Board Game

Learners to answer questions on Sustainable Development Goals

Possibilities

Session: Theme-based research and Case Studies

  • Learners will listen to various case studies of inspiring start-ups, companies, or communities where AI has been involved in real life.
  • Learners will be allotted a theme around which they need to search for present AI trends and have to visualize the future of AI in and around their respective themes.

Recommended Activity: Job Ad Creating activity

Learners to create a job advertisement for a firm describing the nature of the job available and the skill-set required for it 10 years down the line. They need to figure out how AI is going to transform the nature of jobs and create the Ad accordingly.

AI Ethics

Recommended Activity: Ethics Awareness

Students play the role of major stakeholders and they have to decide what is ethical and what is not for a given scenario.

Session: AI Bias and AI Access

  • Discussing the possible bias in data collection
  • Discussing the implications of AI

Recommended Activity: Balloon Debate

  • Students divide into teams of 3 and 2 teams are given the same theme. One team goes in affirmation to AI for their section while the other one goes against it.
  • They have to come up with their points as to why AI is beneficial/harmful for society.

Unit 2: AI PROJECT CYCLE

Problem Scoping

Session: Introduction to AI Project Cycle

  • Problem Scoping
  • Data Acquisition
  • Data Exploration
  • Modeling
  • Evaluation

Activity: Brainstorm around the theme provided and set a goal for the AI project.

  • Discuss various topics within the given theme and select one.
  • List down/ Draw a mindmap of problems related to the selected topic and choose one problem to be the goal for the project.

Activity: To set actions around the goal.

  • List down the stakeholders involved in the problem.
  • Search on the current actions taken to solve this problem.
  • Think about the ethics involved in the goal of your project.

Activity: Data and Analysis

  • What are the data features needed?
  • Where can you get the data?
  • How frequently do you have to collect the data?
  • What happens if you don’t have enough data?
  • What kind of analysis needs to be done?
  • How will it be validated?
  • How does the analysis inform the action?

Presentation: Presenting the goal, actions, and data.

Data Acquisition

Activity: Introduction to data and its types.

Students work around the scenarios given to them and think of ways to acquire data.

Data Exploration

Session: Data Visualisation

  • Need of visualizing data
  • Ways to visualize data using various types of graphical tools.

Recommended Activity: Let's use Graphical Tools

  • To decide what kind of data is required for a given scenario and acquire the same.
  • To select an appropriate graphical format to represent the data acquired.
  • Presenting the graph sketched.

Modeling

Session: Decision Tree

To introduce the basic structure of Decision Trees to students.

Recommended Activity: Decision Tree

To design a Decision Tree based on the data given.

Recommended Activity: Pixel It

  • To create an "AI Model" to classify handwritten letters.
  • Students develop a model to classify handwritten letters by diving the alphabets into pixels.
  • Pixels are then joined together to analyze a pattern amongst the same alphabets and to differentiate the different ones.

Unit 3: NEURAL NETWORK

Session: Introduction to neural network

  • Relation between the neural network and nervous system in the human body
  • Describing the function of neural networks.

Recommended Activity: Creating a Human Neural Network

  • Students split into four teams each representing input layer (X students), hidden layer 1 (Y students), hidden layer 2 (Z students), and output layer (1 student) respectively.
  • The input layer gets data that is passed on to hidden layers after some processing. The output layer finally gets all information and gives meaningful information as output.

Unit 4: INTRODUCTION TO PYTHON

Recommended Activity: Introduction to programming using Online Gaming portals like Code Combat.

Session: Introduction to Python language

Introducing python programming and its applications

Practical: Python Basics

  • Students go through lessons on Python Basics (Variables, Arithmetic Operators, Expressions, Data Types - integer, float, strings, using print() and input() functions)
  • Students will try some simple problem-solving exercises on Python Compiler.

Practical: Python Lists

  • Students go through lessons on Python Lists (Simple operations using list)
  • Students will try some basic problem-solving exercises using lists on Python Compiler.
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