UNITS

HOURS

(Theory + Practical)

MAX. MARKS

(Theory + Practical)

Part A

Employability Skills

Unit 1 : Communication Skills-III

10

2

Unit 2 : Self-Management Skills-III

10

2

Unit 3 : ICT Skills-III

10

2

Unit 4 : Entrepreneurial Skills-III

15

2

Unit 5 : Green Skills-III

05

2

Total

50

10

Part B

Subject Specific Skills

To be assessed in Theory Exams

Unit 1: Introduction To AI

30

08

Unit 2: AI Applications & Methodologies

30

10

Unit 4: AI Values (Ethical Decision Making)

05

04

Unit 5: Introduction To Storytelling

20

08

Unit 8: Regression

30

10

To be assessed through Practical only

Unit 3: Maths For AI

10

-

Unit 6: Critical & Creative Thinking

05

-

Unit 7: Data Analysis (Computational Thinking)

30

-

Unit 9: Classification & Clustering

20

-

Unit 10: AI Values (Bias Awareness)

30

-

Total

210

40

Part C

Practical Work

Practical Examination

40

Viva-Voce

Total

40

Part D

Project Work/ Field Visit/ Project/ Ideation + presentation

10

Viva-Voce

Total

10

GRAND TOTAL

260

100

S. No.

Units

Duration in Hours

1.

Unit 1: Communication Skills-III

10

2.

Unit 2: Self-management Skills-III

10

3.

Unit 3: Information and Communication Technology Skills-III

10

4.

Unit 4: Entrepreneurial Skills-III

15

5.

Unit 5: Green Skills-III

05

TOTAL

50

Level I: AI Informed (AI Foundations)

· Unit1: Introduction to AI

· Unit 2: AI Applications & Methodologies

· Unit 3: Math for AI

· Unit 4: AI Values (Ethical Decision Making)

· Unit 5: Introduction to Storytelling

Level 2: AI Inquired (AI Apply)

· Unit 6: Critical & Creative Thinking

· Unit 7: Data Analysis (Computational Thinking)

· Unit 8: Regression

· Unit 9: Classification & Clustering

· Unit 10: AI Values (Bias Awareness)

UNIT

TOPICS

LEARNING OUTCOMES

Unit 1: Introduction

(knowledge)

Introduction-AI for everyone

· What is AI?

o Kids can AI

· History of AI

· What is Machine Learning

o Difference between conventional programming and machine learning

o How is Machine learning related to AI?

· What is data?

o Structured

o Unstructured

o Examples of unstructured data- text, images

· Terminology and Related Concepts Intro to AI

o Machine learning

o Supervised learning (examples)

o Unsupervised learning (examples)

o Deep learning

o Reinforcement learning

o Machine Learning Techniques and Training

o Neural Networks

· What machine learning can and cannot do

· More examples of what machine learning can and cannot do

· Jobs in AI

Knowledge – Define AI andML

Comprehension – What arethe AI products/ applications in society and how are they different from non- AI products/ applications?

Evaluation – What kind ofjobs may appear in the future?

Unit 2: AI Applications and Methodologies

(Introduction) (Knowledge)

Present day AI and Applications

· Key Fields of Application in AI

o Chatbots (Natural Language Processing, speech)

o Alexa, Siri and others

o Computer vision

o Weather Predictions

o Price forecast for commodities

o Self-driving cars

· Characteristics and types of AI

o Data driven

o Autonomous systems

o Recommender systems

o Human like

Knowledge – Where can AIbe applied (like in the field ofComputer vision, Speech, Text, etc.), What is deep learning?

Comprehension – How AIwill impact our society

Analysis – How should weget ready for the AI age (future)

· Cognitive Computing (Perception, Learning, Reasoning) Cognitive computing

· Recommended deep-dive in NLP, CV, etc.*

· AI and Society coursera-ai-for-everyone

· The Future with AI, and AI in Action (Introduction)

· Non-technical explanation of deep learning coursera-ai-for-everyone

Unit 3:

Maths for AI

(Recap)

(Knowledge)

· Introduction to matrices (Recap)

· Introduction to set theory (Recap)

o Introduction to data table joins

· Simple statistical concepts

· Visual representation of data, bar graph, histogram, frequency bins, scatter plots, etc.

· With co-ordinates and graphs introduction to dimensionality of data

· Simple linear equation

o Least square method of regression

Comprehension – Linear

Algebra, Statistics, Basics of

Graphs and Set theory

Application – Application of

Math in AI

Synthesis – Representing

data in term of mathematical

formula

Unit 4: AI Values (Ethical decision making)

(Values)

AI: Issues, Concerns and Ethical Considerations

· Issues and Concerns around AI

· AI and Ethical Concerns

· AI and Bias

· AI: Ethics, Bias, and Trust

· Employment and AI

Knowledge – Ethics, Bias, Impacts of bias on society Application – Spot issue in data, Make arguments, Apply rules

Unit 5:

Introduction

to story

telling

(Skills)

· Storytelling: communication across the ages

o Learn why storytelling is so powerful and cross-cultural, and what this means for

data storytelling

· The Need for Storytelling

· Story telling with data

o By the numbers: How to tell a great story with your data.

· Conflict and Resolution

o Everyone wants to resolve conflict, and a good data storyteller is there to help!

· Storytelling for audience

o Your data storytelling depends on the background knowledge of your audience.

· Insights from storytelling

o Make the audience care about the data

o Keep the audience engaged

o Create from the end; present from the

beginning

o Start with an anecdote, end with the data

o Build suspense, not surprise

Skill – Imagination, mapping

the plot into key events

increasing memory retention.

Application- Helping in creating blogs, videos, and other content.

UNIT

TOPICS

LEARNING OUTCOMES

Unit 6: Critical and Creative thinking (Skills)

· Design thinking framework

o Right questioning (5W and 1H)

o Identifying the problem to solve

o Ideate

Skill – Understanding the problem and being able to express the same

Creativity – To be able to

develop/innovate from design a solution

Unit 7: Data Analysis (Computational thinking) (Skills)

· Types of structured data

o Date and time

o String

o Categorical

· Representation of data

· Exploring Data Exploring data (Pattern recognition)

o Cases, variables and levels of measurement

o Data matrix and frequency table

o Graphs and shapes of distributions

o Mode, median and mean

o Range, interquartile range and box plot

o Variance and standard deviation

o Z-scores

o Example

o Practice exercise

Knowledge – Types of structured data, statistical principals – frequency tables, mean, median, mode, range, etc. Application – Representing data in terms of graphs, statistical models

Synthesis – To be able to represent a simple problem in terms of numbers

Unit 8: Regression (Knowledge)

· Correlation and Regression

o Crosstabs and scatterplots

o Pearson's r

o Regression - Finding the line

o Regression - Describing the line

o Regression - How good is the line?

o Correlation is not causation

o Example contingency table

o Example Pearson's r and regression Readings

o Correlation

o Regression

o Caveats and examples

o Practice exercise Correlation and Regression

o Explain the importance of data from above examples

o How prediction changes with changing data?

Knowledge – Correlations, Regression, and other related terms

Applications – Being able to relate data with regression and correlation. Everyday applications of these mathematical concepts.

UNIT

TOPICS

LEARNING OUTCOMES

Unit 9: Classification& Clustering

(Knowledge)

· What is a classification problem?

· Examples

- Simple binary classification

· Introduction to binary classification with logistic regression

· True positives, true negatives, false positives and false negatives

o Where we should care more with examples

o Example- false negative of a disease detection can have different implication than false positive, one will be more physical harm and other will be mental

· Practice exercise on simple Binary Classification model

Knowledge – What is classification and its types, what kind of problems may be placed under the category of a classification problem

Applications – Where to apply classification principals

Analysis – Impact of the application of incorrect algorithms on society

· What is a clustering problem?

· Why is it unsupervised?

· Examples

· Practice exercise on simple Clustering model

Knowledge – Clustering problems and its application, why is it called clustering

Application – Application of clustering problem using standard models

Unit 10: AI Values (Bias awareness)

· AI working for good

· Principles for ethical AI

· Types of bias (personal /cultural

· /societal)

· How bias influences AI based decisions

· How data driven decisions can be debiased

· Hands on exercise to Detect the Bias

(Intro to AI)

Knowledge – What is ethics, Impact of ethics on society, the impact of bias on AI functioning

(Values)

Evaluation – Biases in data, how to de-bias or neutralize the biased data

Application – Finding bias in acquired dataset

CBSE Class 12 Artificial Intelligence Syllabus 2023-24: Download PDF