UNITS

NO. OF HOURS

for Theory and Practical

MAX. MARKS for

Theory and Practical

PART A

Employability Skills

Unit 1: Communication Skills-II

10

2

Unit 2: Self-Management Skills-II

10

2

Unit 3: ICT Skills-II

10

2

Unit 4: Entrepreneurial Skills-II

15

2

Unit 5: Green Skills-II

5

2

Total

50

10

PART B

Subject Specific Skills

Theory

Practical

Unit 1: Introduction to Artificial Intelligence (AI)

15

-

7

Unit 2: AI Project Cycle

15

-

9

Unit 3: Advance Python

(To be assessed in Practicals only)

-

30

--

Unit 4: Data Science (Introduction, Applications of Data Sciences, Data Science: Getting Started (up to Data Access),

the remaining portion is to be assessed in practical

7

8

4

Unit 5: Computer Vision (Introduction, Applications of Computer Vision, Computer Vision: Getting Started (up to RGB Images),

the remaining portion is to be assessed in practical

12

18

4

Unit 6: Natural Language Processing

25

5

8

Unit 7: Evaluation

15

8

Total

150

40

PART C

Practical Work:

Practical File with minimum 15 Programs

15

Practical Examination

· Unit 3: Advance Python

· Unit 4: Data Science

· Unit 5: Computer Vision

5

5

5

5

Viva Voce

5

Total

35

PART D

Project Work / Field Visit / Student Portfolio (Anyone to be done)

10

Viva Voce

5

Total

15

GRAND TOTAL

210

100

S. No.

Units

Duration in Hours

1

Unit 1: Communication Skills-II

10

2

Unit 2: Self-management Skills-II

10

3

Unit 3: Information and Communication Technology Skills II

10

4

Unit 4: Entrepreneurial Skills-II

15

5

Unit 5: Green Skills-II

5

TOTAL

50

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

Foundational concepts of AI

Understand the concept of human intelligence and its various components such as reasoning, problem-solving, and creativity.

Session: What is Intelligence?

Session: Decision Making.

● How do you make decisions?

● Make your choices!

Session: what is Artificial Intelligence and what is not?

Basics of AI: Let’s Get Started

Understand the concept of Artificial Intelligence (AI) and its domains

Session: Introduction to AI and related terminologies.

● Introducing AI, ML & DL.

● Introduction to AI Domains (Data Sciences, CV & NLP)

● Gamified tools for each domain-

o Data Sciences- Impact Filter (Impact of rise in temperature on different species) https://artsexperiments.withgoogle.com

/impact filter/

o CV- Autodraw (It pairs machine learning with drawings from talented artists to help you draw stuff fast.)

https://www.autodraw.com/

o NLP- Wordtune (AI writing tool that rewrites, rephrases, and rewards your writing)

https://www.wordtune.com/

Explore the use of AI in real Life.

Session: Applications of AI – A look at Real-life AI implementations

Learn about the ethical concerns involved in AI development, such as AI bias, and data privacy and how they can be addressed.

Session: AI Ethics

● Moral Machine Activity: a platform for gathering a human perspective on moral decisions made by machine intelligence, such as self-driving cars.

http://moralmachine.mit.edu/

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

Introduction

Understand the stages involved in the AI project cycle, such as problem scoping, data collection, data

exploration, modelling, and evaluation.

Session: Introduction to AI Project Cycle

Problem Scoping

Learn about the importance of project planning in AI development and how to define project goals and

objectives.

Session: Understanding Problem Scoping & Sustainable Development Goals

Data Acquisition

Develop an understanding of the importance of data

collection in AI and how to choose the right data sources.

Session: Simplifying Data Acquisition

Data Exploration

Know various data exploration techniques and their importance

Session: Visualising Data

Modelling

Know about the different machine learning algorithms used to train AI models

Session: Introduction to modelling

● Introduction to Rule-Based & Learning Based AI Approaches

● Activity: Teachable machine to demonstrate Supervised Learning

https://teachablemachine.withgoogl e.com/

● Activity: Infinite Drum Machine to demonstrate Unsupervised learning https://experiments.withgoogle.com

/ai/drum-machine/view/

● Introduction to Supervised, Unsupervised & Reinforcement Learning Models(Optional)**

● Neural Networks

Evaluation

Know the importance of evaluation and various metrics

available for evaluation

Session: Evaluating the idea!

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

Recap

Understand how to work with Jupyter Notebook, create a virtual environment, and install Python.

Packages.

Session: Jupyter Notebook

Able to write basic Python programs using fundamental concepts such as variables, data

types, operators, and control structures.

Session: Introduction to Python

Able to use Python built-in functions and libraries.

Session: Python Basics

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

Introduction

Define the concept of Data Science and understand its applications in various fields.

Session: Introduction to Data Science

Session: Applications of Data Science

Getting Started

Understand the basic concepts of data acquisition, visualization, and exploration.

Session: Revisiting AI Project Cycle, Data Collection, Data Access

Activities:

Game: Rock, Paper & Scissors https://next.rockpaperscissors.ai/

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

Python Packages

Use Python libraries such as NumPy, Pandas, and Matplotlib for data analysis and visualization.

Session: Python for Data Sciences

· Numpy

· Pandas

· Matplotlib

Concepts of Data Sciences

Understand the basic concepts of statistics, such as mean, median, mode, and standard deviation, and apply them to analyze data using

various Python packages.

Session: Statistical Learning & Data Visualisation

K-nearest neighbour model

(Optional)**

Understand the basic concepts of the KNN algorithm

and its applications in supervised learning.

Activity: Personality Prediction (Optional)**

Session: Understanding K-nearest neighbour model (Optional)**

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

Introduction

Define the concept of Computer Vision and understand its applications in

various fields.

Session: Introduction to Computer Vision

Session: Applications of CV

Concepts of Computer Vision

Understand the basic concepts of image representation, feature extraction, object detection, and segmentation.

Session: Understanding CV Concepts

● Computer Vision Tasks

● Basics of Images-Pixel, Resolution, Pixel value

● Grayscale and RGB images

Activities:

● Game- Emoji Scavenger Hunt https://emojiscavengerhunt.withgoogle.com/

● RGB Calculator: https://www.w3schools.com/colors/color s_rgb.asp

● Create your pixel art: www.piskelapp.com

● Create your convolutions: http://setosa.io/ev/image-kernels/

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

OpenCV

Use Python libraries such as OpenCV for basic image processing and computer vision tasks.

Session: Introduction to OpenCV

Hands-on: Image Processing

Convolution Operator (Optional)**

Apply the convolution operator to process images and extract useful features.

Session: Understanding Convolution operator

(Optional)**

Activity: Convolution Operator (Optional)**

Convolution Neural Network (Optional)**

Understand the basic architecture of a CNN and its applications in computer vision and image recognition.

Session: Introduction to CNN (Optional)**

Session: Understanding CNN (Optional)**

● Kernel

● Layers of CNN

Activity: Testing CNN (Optional)**

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

Introduction

Understand the concept of Natural Language Processing (NLP) and its importance in the field of Artificial Intelligence (AI).

Session: Introduction to Natural Language Processing

Activity: Use Google Translate for the same spelling words

Session: NLP Applications

Session: Revisiting AI Project Cycle

Chatbots

Explore the various applications of NLP in everyday life, such as chatbots, sentiment analysis, and automatic

summarization

Activity: Introduction to Chatbots

Language Differences

Gain an understanding of the challenges involved in understanding

human language by machine.

Session: Human Language VS Computer Language

Concepts of Natural Language Processing

Learn about the Text Normalization technique used in NLP and the popular NLP model - Bag-of-Words

Session: Data Processing

· Text Normalisation

· Bag of Words

Hands-on: Text processing

● Data Processing

● Bag of Words

● TFIDF (Optional)**

● NLTK (Optional)**

SUB-UNIT

LEARNING OUTCOMES

SESSION/ ACTIVITY/ PRACTICAL

Introduction

Understand the role of evaluation in the development

and implementation of AI systems.

Session: Introduction to Model Evaluation

● What is Evaluation?

● Different types of Evaluation techniques- Underfit, Perfect Fit, OverFit

Model Evaluation Terminology

Learn various Model Evaluation Terminologies

Session: Model Evaluation Terminologies

● The Scenario - Prediction, Reality, True Positive, True Negative, False Positive, False Negative

● Confusion Matrix

● Activity- to make a confusion matrix based on data given for the Containment Zone Prediction Model

Confusion Matrix

Learn to make a confusion matrix for a given Scenario

Session & Activity: Confusion Matrix

Evaluation Methods

Learn about the different types of evaluation techniques in AI, such as Accuracy, Precision, Recall and F1 Score, and their significance.

Session: Evaluation Methods

● Accuracy

● Precision

● Recall

● Which Metric is Important? - Precision or Recall

● F1 Score

Activity: Practice Evaluation

Suggested Programs List

● Write a program to add the elements of the two lists.

● Write a program to calculate mean, median and mode using Numpy

● Write a program to display a line chart from (2,5) to (9,10).

● Write a program to display a scatter chart for the following points (2,5), (9,10),(8,3),(5,7),(6,18).

● Read the CSV file saved in your system and display 10 rows.

● Read the CSV file saved in your system and display its information

● Write a program to read an image and display using Python

● Write a program to read an image and identify its shape using Python

Important Links

·https://cbseacademic.nic.in/web_material/Curriculum21/publication/secondar y/Class10_Facilitator_Handbook.pdf

· Link to AI Activities & Jupyter Notebooks (including sample projects) 

https://bit.ly/class_X_activities_jupyter_notebooks

Sample Projects

  1. Student Marks Prediction Model

  2. CNN Model on Smoke and Fire Detection

Field Work

Students’ participation in the following-

· AI for Youth Bootcamp

· AI Fests/ Exhibition

· Participation in any AI training sessions

· Virtual tours of companies using AI to get acquainted with real-life usage

Student Portfolio (to be continued from class IX)

· Maintaining a record of all AI activities

· Hackathons

· Competitions (CBSE/Interschool)

 Note: The portfolio should contain a minimum 5 activities